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bunkalab

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arxiv_topic_modeling

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Ex-Twit: Explainable Twitter Mining on Health Data

Tunazzina Islam

Since most machine learning models provide no explanations for the predictions, their predictions are obscure for the human. The ability to explain a model's prediction has become a necessity in many applications including Twitter mining. In this work, we propose a method called Explainable Twitter Mining (Ex-Twit) combining Topic Modeling and Local Interpretable Model-agnostic Explanation (LIME) to predict the topic and explain the model predictions. We demonstrate the effectiveness of Ex-Twit on Twitter health-related data.

http://arxiv.org/abs/1906.02132v2

cs.CL, cs.AI, cs.LG

Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models

Varun Kumar, Alison Smith-Renner, Leah Findlater, Kevin Seppi, Jordan Boyd-Graber

To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations' results match users' expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.

http://arxiv.org/abs/1905.09864v2

cs.CL, cs.HC, cs.IR, cs.LG

Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter

Francois Modave, Yunpeng Zhao, Janice Krieger, Zhe He, Yi Guo, Jinhai Huo, Mattia Prosperi, Jiang Bian

Among American women, the rate of breast cancer is only second to lung cancer. An estimated 12.4% women will develop breast cancer over the course of their lifetime. The widespread use of social media across the socio-economic spectrum offers unparalleled ways to facilitate information sharing, in particular as it pertains to health. Social media is also used by many healthcare stakeholders, ranging from government agencies to healthcare industry, to disseminate health information and to engage patients. The purpose of this study is to investigate people's perceptions and attitudes relate to breast cancer, especially those that are related to physical activities, on Twitter. To achieve this, we first identified and collected tweets related to breast cancer; and then used topic modeling and sentiment analysis techniques to understanding discussion themes and quantify Twitter users' perceptions and emotions w.r.t breast cancer to answer 5 research questions.

http://arxiv.org/abs/1905.12469v1

cs.CY, stat.ML

From web crawled text to project descriptions: automatic summarizing of social innovation projects

Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic

In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner. A database of social innovation is an important source of information that can expand collaboration between social innovators, drive policy and serve as an important resource for research. Such a database needs to have projects described and summarized. In this paper, we propose and compare several methods (e.g. SVM-based, recurrent neural network based, ensambled) for describing projects based on the text that is available on project websites. We also address and propose a new metric for automated evaluation of summaries based on topic modelling.

http://arxiv.org/abs/1905.09086v1

cs.CL, cs.IR, cs.LG

Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user-POI relationship for spatio-temporal sensitive POI recommendation. Existing recommendation algorithms, including both shallow and deep approaches, usually embed the visiting records of a user into a single latent vector to model user preferences: this has limited power of representation and interpretability. In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. We further incorporate a geographical module to exploit user-specific spatial preference and POI-specific spatial influence to enhance recommendations. The proposed unified hybrid model is widely applicable to various POI recommendation scenarios. Extensive experiments on real-world WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and 29.95% for context-aware and sequential recommendation, respectively). Also, qualitative analysis of the attention weights and topic modeling provides insight into the model's recommendation process and results.

http://arxiv.org/abs/1905.13127v1

cs.IR, cs.LG, stat.ML

Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator. We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance. This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion. By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.

http://arxiv.org/abs/1905.08622v3

cs.CV, cs.CL, cs.LG, stat.ML

Microblog Hashtag Generation via Encoding Conversation Contexts

Yue Wang, Jing Li, Irwin King, Michael R. Lyu, Shuming Shi

Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.

http://arxiv.org/abs/1905.07584v1

cs.CL

Automatic Evaluation of Local Topic Quality

Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, Kevin Seppi

Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments. Token-level assignments are important for downstream tasks such as classification. Even recent models, which aim to improve the quality of these token-level topic assignments, have been evaluated only with respect to global metrics. We propose a task designed to elicit human judgments of token-level topic assignments. We use a variety of topic model types and parameters and discover that global metrics agree poorly with human assignments. Since human evaluation is expensive we propose a variety of automated metrics to evaluate topic models at a local level. Finally, we correlate our proposed metrics with human judgments from the task on several datasets. We show that an evaluation based on the percent of topic switches correlates most strongly with human judgment of local topic quality. We suggest that this new metric, which we call consistency, be adopted alongside global metrics such as topic coherence when evaluating new topic models.

http://arxiv.org/abs/1905.13126v1

cs.IR, cs.CL, cs.LG, stat.ML

Cross-referencing using Fine-grained Topic Modeling

Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Emily Hales, Kevin Seppi

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.

http://arxiv.org/abs/1905.07508v1

cs.CL, cs.IR, cs.LG, stat.ML

Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection

Jeffrey Liu, Andrew Weinert, Saurabh Amin

Traffic Management Centers (TMCs) routinely use traffic cameras to provide situational awareness regarding traffic, road, and weather conditions. Camera footage is quite useful for a variety of diagnostic purposes; yet, most footage is kept for only a few days, if at all. This is largely due to the fact that currently, identification of notable footage is done via manual review by human operators---a laborious and inefficient process. In this article, we propose a semantics-oriented approach to analyzing sequential image data, and demonstrate its application for automatic detection of real-world, anomalous events in weather and traffic conditions. Our approach constructs semantic vector representations of image contents from textual labels which can be easily obtained from off-the-shelf, pretrained image labeling software. These semantic label vectors are used to construct semantic topic signals---time series representations of physical processes---using the Latent Dirichlet Allocation (LDA) topic model. By detecting anomalies in the topic signals, we identify notable footage corresponding to winter storms and anomalous traffic congestion. In validation against real-world events, anomaly detection using semantic topic signals significantly outperforms detection using any individual label signal.

http://arxiv.org/abs/1905.07332v1

cs.CV

A New Anchor Word Selection Method for the Separable Topic Discovery

Kun He, Wu Wang, Xiaosen Wang, John E. Hopcroft

Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high-dimensional word co-occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word-pair is very low, then the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word-pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word-pair similarity. Following this way, we design a greedy method to find a minimum edge-weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real-world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull-based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF based method.

http://arxiv.org/abs/1905.06109v1

cs.IR, cs.CL, cs.LG, stat.ML

Where does active travel fit within local community narratives of mobility space and place?

Alec Biehl, Ying Chen, Karla Sanabria-Veaz, David Uttal, Amanda Stathopoulos

Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private motorized vehicles towards active modes. The current literature on `soft' policy emphasizes the importance of tailoring behavior change campaigns to individual or geographic context. Yet, there is a lack of insight and appropriate tools to promote active mobility and overcome transport disadvantage from the local community perspective. The current study investigates the promotion of walking and cycling adoption using a series of focus groups with local residents in two geographic communities, namely Chicago's (1) Humboldt Park neighborhood and (2) suburb of Evanston. The research approach combines traditional qualitative discourse analysis with quantitative text-mining tools, namely topic modeling and sentiment analysis. The analysis uncovers the local mobility culture, embedded norms and values associated with acceptance of active travel modes in different communities. We observe that underserved populations within diverse communities view active mobility simultaneously as a necessity and as a symbol of privilege that is sometimes at odds with the local culture. The mixed methods approach to analyzing community member discourses is translated into policy findings that are either tailored to local context or broadly applicable to curbing automobile dominance. Overall, residents of both Humboldt Park and Evanston envision a society in which multimodalism replaces car-centrism, but differences in the local physical and social environments would and should influence the manner in which overarching policy objectives are met.

http://arxiv.org/abs/1905.02674v1

econ.GN, q-fin.EC, stat.ML

Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors

Trung Trinh, Tho Quan, Trung Mai

Most of the information on the Internet is represented in the form of microtexts, which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modeling is one of the popular methods to extract knowledge from a collection of documents; however, conventional topic models such as latent Dirichlet allocation (LDA) are unable to perform well on short documents, mostly due to the scarcity of word co-occurrence statistics embedded in the data. The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets. To solve the lack of information of microtexts, we allow our method to take advantage of word embeddings for additional knowledge of relationships between words. For speed and scalability, we apply autoencoding variational Bayes, an algorithm that can perform efficient black-box inference in probabilistic models. The result of our work is a novel topic model called the nested variational autoencoder, which is a distribution that takes into account word vectors and is parameterized by a neural network architecture. For optimization, the model is trained to approximate the posterior distribution of the original LDA model. Experiments show the improvements of our model on microtexts as well as its runtime advantage.

http://arxiv.org/abs/1905.00195v3

cs.CL

PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization

Gordon E. Moon, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed in the past. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we devise a parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating significant performance improvement over existing state-of-the-art parallel NMF algorithms.

http://arxiv.org/abs/1904.07935v1

cs.LG, cs.DC, stat.ML

Sameness Entices, but Novelty Enchants in Fanfiction Online

Elise Jing, Simon DeDeo, Devin Robert Wright, Yong-Yeol Ahn

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.

http://arxiv.org/abs/1904.07741v2

cs.CL, cs.SI

Tracing Forum Posts to MOOC Content using Topic Analysis

Alexander William Wong, Ken Wong, Abram Hindle

Massive Open Online Courses are educational programs that are open and accessible to a large number of people through the internet. To facilitate learning, MOOC discussion forums exist where students and instructors communicate questions, answers, and thoughts related to the course. The primary objective of this paper is to investigate tracing discussion forum posts back to course lecture videos and readings using topic analysis. We utilize both unsupervised and supervised variants of Latent Dirichlet Allocation (LDA) to extract topics from course material and classify forum posts. We validate our approach on posts bootstrapped from five Coursera courses and determine that topic models can be used to map student discussion posts back to the underlying course lecture or reading. Labeled LDA outperforms unsupervised Hierarchical Dirichlet Process LDA and base LDA for our traceability task. This research is useful as it provides an automated approach for clustering student discussions by course material, enabling instructors to quickly evaluate student misunderstanding of content and clarify materials accordingly.

http://arxiv.org/abs/1904.07307v1

cs.IR, cs.CL, cs.CY

Modeling Hierarchical Usage Context for Software Exceptions based on Interaction Data

Hui Chen, Kostadin Damevski, David Shepherd, Nicholas A. Kraft

Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production - software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers' comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the Nested Hierarchical Dirichlet Process, which is a Bayesian non-parametric topic model commonly applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.

http://arxiv.org/abs/1904.07072v2

cs.SE

A framework for streamlined statistical prediction using topic models

Vanessa Glenny, Jonathan Tuke, Nigel Bean, Lewis Mitchell

In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves as predictors, alongside typical statistical tools for predictive modelling. We apply this framework in a Social Sciences context (applied animal behaviour) as well as a Humanities context (narrative analysis) as examples of this framework. The results show that topic regression models perform comparably to their much less efficient equivalents that use individual words as predictors.

http://arxiv.org/abs/1904.06941v1

stat.AP, cs.CL

Finding a latent k-simplex in O(k . nnz(data)) time via Subset Smoothing

Chiranjib Bhattacharyya, Ravindran Kannan

In this paper we show that a large class of Latent variable models, such as Mixed Membership Stochastic Block(MMSB) Models, Topic Models, and Adversarial Clustering, can be unified through a geometric perspective, replacing model specific assumptions and algorithms for individual models. The geometric perspective leads to the formulation: \emph{find a latent $k-$ polytope $K$ in ${\bf R}^d$ given $n$ data points, each obtained by perturbing a latent point in $K$}. This problem does not seem to have been considered in the literature. The most important contribution of this paper is to show that the latent $k-$polytope problem admits an efficient algorithm under deterministic assumptions which naturally hold in Latent variable models considered in this paper. ur algorithm runs in time $O^*(k\; \mbox{nnz})$ matching the best running time of algorithms in special cases considered here and is better when the data is sparse, as is the case in applications. An important novelty of the algorithm is the introduction of \emph{subset smoothed polytope}, $K'$, the convex hull of the ${n\choose \delta n}$ points obtained by averaging all $\delta n$ subsets of the data points, for a given $\delta \in (0,1)$. We show that $K$ and $K'$ are close in Hausdroff distance. Among the consequences of our algorithm are the following: (a) MMSB Models and Topic Models: the first quasi-input-sparsity time algorithm for parameter estimation for $k \in O^*(1)$, (b) Adversarial Clustering: In $k-$means, if, an adversary is allowed to move many data points from each cluster an arbitrary amount towards the convex hull of the centers of other clusters, our algorithm still estimates cluster centers well.

http://arxiv.org/abs/1904.06738v4

cs.LG, cs.DS, stat.ML

Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

Qiang Jipeng, Qian Zhenyu, Li Yun, Yuan Yunhao, Wu Xindong

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.

http://arxiv.org/abs/1904.07695v1

cs.IR, cs.CL

Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling

Daniel Pfeifer, Jochen L. Leidner

We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via topics is optimized. The algorithm starts with one-word topics and joins two topics at every step. It therefore generates a solution for every desired number of topics ranging between the size of the training vocabulary and one. The process represents an agglomerative clustering that corresponds to a binary tree of topics. A resulting tree may act as a containment hierarchy, typically with more general topics towards the root of tree and more specific topics towards the leaves. Topic Grouper is not governed by a background distribution such as the Dirichlet and avoids hyper parameter optimizations. We show that Topic Grouper has reasonable predictive power and also a reasonable theoretical and practical complexity. Topic Grouper can deal well with stop words and function words and tends to push them into their own topics. Also, it can handle topic distributions, where some topics are more frequent than others. We present typical examples of computed topics from evaluation datasets, where topics appear conclusive and coherent. In this context, the fact that each word belongs to exactly one topic is not a major limitation; in some scenarios this can even be a genuine advantage, e.g.~a related shopping basket analysis may aid in optimizing groupings of articles in sales catalogs.

http://arxiv.org/abs/1904.06483v1

cs.IR, cs.LG

Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments

Jerrold Soh Tsin Howe, Lim How Khang, Ian Ernst Chai

This paper conducts a comparative study on the performance of various machine learning (``ML'') approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.

http://arxiv.org/abs/1904.06470v1

cs.CL

Mixing syntagmatic and paradigmatic information for concept detection

Louis Chartrand, Mohamed Bouguessa

In the last decades, philosophers have begun using empirical data for conceptual analysis, but corpus-based conceptual analysis has so far failed to develop, in part because of the absence of reliable methods to automatically detect concepts in textual data. Previous attempts have shown that topic models can constitute efficient concept detection heuristics, but while they leverage the syntagmatic relations in a corpus, they fail to exploit paradigmatic relations, and thus probably fail to model concepts accurately. In this article, we show that using a topic model that models concepts on a space of word embeddings (Hu and Tsujii, 2016) can lead to significant increases in concept detection performance, as well as enable the target concept to be expressed in more flexible ways using word vectors.

http://arxiv.org/abs/1904.04461v2

cs.CL

Hybrid Approaches to Detect Comments Violating Macro Norms on Reddit

Eshwar Chandrasekharan, Eric Gilbert

In this dataset paper, we present a three-stage process to collect Reddit comments that are removed comments by moderators of several subreddits, for violating subreddit rules and guidelines. Other than the fact that these comments were flagged by moderators for violating community norms, we do not have any other information regarding the nature of the violations. Through this procedure, we collect over 2M comments removed by moderators of 100 different Reddit communities, and publicly release the data. Working with this dataset of removed comments, we identify 8 macro norms---norms that are widely enforced on most parts of Reddit. We extract these macro norms by employing a hybrid approach---classification, topic modeling, and open-coding---on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we label over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available. By breaking down a collection of removed comments into more granular types of macro norm violation, our dataset can be used to train more nuanced machine learning classifiers for online moderation.

http://arxiv.org/abs/1904.03596v2

cs.SI

Health and Kinship Matter: Learning About Direct-To-Consumer Genetic Testing User Experiences via Online Discussions

Zhijun Yin, Lijun Song, Ellen Clayton, Bradley Malin

Direct-to-consumer (DTC) genetic testing has gained in popularity over the past decade, with over 12 million consumers to date. Given its increasing stature in society, along with weak regulatory oversight, it is important to learn about actual consumers' testing experiences. Traditional interviews or survey-based studies have been limited in that they had small sample sizes or lacked detailed descriptions of personal experiences. Yet many people are now sharing their DTC genetic testing experiences via online social media platforms. In this paper, we focused on one particularly lively online discussion forum, \textit{r/23andme} subreddit, where, as of before March 2018, 5,857 users published 37,183 posts. We applied topic modeling to the posts and examined the identified topics and temporal posting trends. We further applied regression analysis to learn the association between the attention that a submission received, in terms of votes and comments, and the posting content. Our findings indicate that bursts of the increase of such online discussion in 2017 may correlate with the Food and Drug Administration's authorization for marketing of 23andMe genetic test on health risks, as well as the hot sale of 23andMe's products on Black Friday. While ancestry composition was a popular subject, kinship was steadily growing towards a major online discussion topic. Moreover, compared with other topics, health and kinship were more likely to receive attention, in terms of votes, while testing reports were more likely to receive attention, in term of comments. Our findings suggest that people may not always be prepared to deal with the unexpected consequences of DTC genetic testing. Moreover, it appears that the users in this subreddit might not sufficiently consider privacy when taking a test or seeking an interpretation from a third-party service provider.

http://arxiv.org/abs/1904.02065v1

cs.CY

Minimum Volume Topic Modeling

Byoungwook Jang, Alfred Hero

We propose a new topic modeling procedure that takes advantage of the fact that the Latent Dirichlet Allocation (LDA) log likelihood function is asymptotically equivalent to the logarithm of the volume of the topic simplex. This allows topic modeling to be reformulated as finding the probability simplex that minimizes its volume and encloses the documents that are represented as distributions over words. A convex relaxation of the minimum volume topic model optimization is proposed, and it is shown that the relaxed problem has the same global minimum as the original problem under the separability assumption and the sufficiently scattered assumption introduced by Arora et al. (2013) and Huang et al. (2016). A locally convergent alternating direction method of multipliers (ADMM) approach is introduced for solving the relaxed minimum volume problem. Numerical experiments illustrate the benefits of our approach in terms of computation time and topic recovery performance.

http://arxiv.org/abs/1904.02064v1

stat.ML, cs.IR, cs.LG

Stochastic Blockmodels with Edge Information

Guy W. Cole, Sinead A. Williamson

Stochastic blockmodels allow us to represent networks in terms of a latent community structure, often yielding intuitions about the underlying social structure. Typically, this structure is inferred based only on a binary network representing the presence or absence of interactions between nodes, which limits the amount of information that can be extracted from the data. In practice, many interaction networks contain much more information about the relationship between two nodes. For example, in an email network, the volume of communication between two users and the content of that communication can give us information about both the strength and the nature of their relationship. In this paper, we propose the Topic Blockmodel, a stochastic blockmodel that uses a count-based topic model to capture the interaction modalities within and between latent communities. By explicitly incorporating information sent between nodes in our network representation, we are able to address questions of interest in real-world situations, such as predicting recipients for an email message or inferring the content of an unopened email. Further, by considering topics associated with a pair of communities, we are better able to interpret the nature of each community and the manner in which it interacts with other communities.

http://arxiv.org/abs/1904.02016v1

cs.SI, cs.LG, stat.ML

Re-Ranking Words to Improve Interpretability of Automatically Generated Topics

Areej Alokaili, Nikolaos Aletras, Mark Stevenson

Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models. Conventionally, topics are represented by their n most probable words, however, these representations are often difficult for humans to interpret. This paper explores the re-ranking of topic words to generate more interpretable topic representations. A range of approaches are compared and evaluated in two experiments. The first uses crowdworkers to associate topics represented by different word rankings with related documents. The second experiment is an automatic approach based on a document retrieval task applied on multiple domains. Results in both experiments demonstrate that re-ranking words improves topic interpretability and that the most effective re-ranking schemes were those which combine information about the importance of words both within topics and their relative frequency in the entire corpus. In addition, close correlation between the results of the two evaluation approaches suggests that the automatic method proposed here could be used to evaluate re-ranking methods without the need for human judgements.

http://arxiv.org/abs/1903.12542v1

cs.CL, cs.IR

Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach

Luke Kumar, Russell Greiner

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional GE data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (~document) as a mixture over cancer-topics, where each cancer-topic is a mixture over GE values (~words). This required some extensions to the standard LDA model eg: to accommodate the "real-valued" expression values - leading to our novel "discretized" Latent Dirichlet Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which describes breast cancer patients using the r=49,576 GE values, from microarrays. Our results show that our approach provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this approach by running it on the Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq modality - and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach.

http://arxiv.org/abs/1903.10536v2

cs.LG, stat.ML

Characterization of Local Attitudes Toward Immigration Using Social Media

Yerka Freire, Eduardo Graells-Garrido

Migration is a worldwide phenomenon that may generate different reactions in the population. Attitudes vary from those that support multiculturalism and communion between locals and foreigners, to contempt and hatred toward immigrants. Since anti-immigration attitudes are often materialized in acts of violence and discrimination, it is important to identify factors that characterize these attitudes. However, doing so is expensive and impractical, as traditional methods require enormous efforts to collect data. In this paper, we propose to leverage Twitter to characterize local attitudes toward immigration, with a case study on Chile, where immigrant population has drastically increased in recent years. Using semi-supervised topic modeling, we situated 49K users into a spectrum ranging from in-favor to against immigration. We characterized both sides of the spectrum in two aspects: the emotions and lexical categories relevant for each attitude, and the discussion network structure. We found that the discussion is mostly driven by Haitian immigration; that there are temporal trends in tendency and polarity of discussion; and that assortative behavior on the network differs with respect to attitude. These insights may inform policy makers on how people feel with respect to migration, with potential implications on communication of policy and the design of interventions to improve inter-group relations.

http://arxiv.org/abs/1903.05072v1

cs.SI, cs.CY

Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns

Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim, Melih Barsbey, Umut Simsekli

We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation. BAM is based on a Poisson process, whose events are marked by using a Bayesian network, where the conditional probability tables of this network are then integrated out analytically. We show that the resulting marginal process turns out to be a Polya urn, an integer valued self-reinforcing process. This urn processes, which we name a Polya-Bayes process, obey certain conditional independence properties that provide further insight about the nature of NTF. These insights also let us develop space efficient simulation algorithms that respect the potential sparsity of data: we propose a class of sequential importance sampling algorithms for computing NTF and approximating their marginal likelihood, which would be useful for model selection. The resulting methods can also be viewed as a model scoring method for topic models and discrete Bayesian networks with hidden variables. The new algorithms have favourable properties in the sparse data regime when contrasted with variational algorithms that become more accurate when the total sum of the elements of the observed tensor goes to infinity. We illustrate the performance on several examples and numerically study the behaviour of the algorithms for various data regimes.

http://arxiv.org/abs/1903.04478v1

stat.ML, cs.LG, stat.CO, stat.ME

Quantum Latent Semantic Analysis

Fabio A. González, Juan C. Caicedo

The main goal of this paper is to explore latent topic analysis (LTA), in the context of quantum information retrieval. LTA is a valuable technique for document analysis and representation, which has been extensively used in information retrieval and machine learning. Different LTA techniques have been proposed, some based on geometrical modeling (such as latent semantic analysis, LSA) and others based on a strong statistical foundation. However, these two different approaches are not usually mixed. Quantum information retrieval has the remarkable virtue of combining both geometry and probability in a common principled framework. We built on this quantum framework to propose a new LTA method, which has a clear geometrical motivation but also supports a well-founded probabilistic interpretation. An initial exploratory experimentation was performed on three standard data sets. The results show that the proposed method outperforms LSA on two of the three datasets. These results suggests that the quantum-motivated representation is an alternative for geometrical latent topic modeling worthy of further exploration.

http://arxiv.org/abs/1903.03082v1

cs.LG, cs.IR, stat.ML

Twitter Speaks: A Case of National Disaster Situational Awareness

Amir Karami, Vishal Shah, Reza Vaezi, Amit Bansal

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.

http://arxiv.org/abs/1903.02706v1

cs.SI, cs.CY, stat.AP, stat.ML

A Review of Stochastic Block Models and Extensions for Graph Clustering

Clement Lee, Darren J Wilkinson

There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.

http://arxiv.org/abs/1903.00114v2

stat.ML, cs.LG

Session-based Social Recommendation via Dynamic Graph Attention Networks

Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

http://arxiv.org/abs/1902.09362v2

cs.IR

Asymptotic Theory of Eigenvectors for Random Matrices with Diverging Spikes

Jianqing Fan, Yingying Fan, Xiao Han, Jinchi Lv

Characterizing the asymptotic distributions of eigenvectors for large random matrices poses important challenges yet can provide useful insights into a range of statistical applications. To this end, in this paper we introduce a general framework of asymptotic theory of eigenvectors (ATE) for large spiked random matrices with diverging spikes and heterogeneous variances, and establish the asymptotic properties of the spiked eigenvectors and eigenvalues for the scenario of the generalized Wigner matrix noise. Under some mild regularity conditions, we provide the asymptotic expansions for the spiked eigenvalues and show that they are asymptotically normal after some normalization. For the spiked eigenvectors, we establish asymptotic expansions for the general linear combination and further show that it is asymptotically normal after some normalization, where the weight vector can be arbitrary. We also provide a more general asymptotic theory for the spiked eigenvectors using the bilinear form. Simulation studies verify the validity of our new theoretical results. Our family of models encompasses many popularly used ones such as the stochastic block models with or without overlapping communities for network analysis and the topic models for text analysis, and our general theory can be exploited for statistical inference in these large-scale applications.

http://arxiv.org/abs/1902.06846v2

math.ST, stat.TH

TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

Michihiro Yasunaga, John Lafferty

Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware equation generation, equation topic inference, and topic-aware alignment of mathematical symbols and words.

http://arxiv.org/abs/1902.06034v3

cs.IR, cs.CL, cs.LG, stat.ML

Towards Autoencoding Variational Inference for Aspect-based Opinion Summary

Tai Hoang, Huy Le, Tho Quan

Aspect-based Opinion Summary (AOS), consisting of aspect discovery and sentiment classification steps, has recently been emerging as one of the most crucial data mining tasks in e-commerce systems. Along this direction, the LDA-based model is considered as a notably suitable approach, since this model offers both topic modeling and sentiment classification. However, unlike traditional topic modeling, in the context of aspect discovery it is often required some initial seed words, whose prior knowledge is not easy to be incorporated into LDA models. Moreover, LDA approaches rely on sampling methods, which need to load the whole corpus into memory, making them hardly scalable. In this research, we study an alternative approach for AOS problem, based on Autoencoding Variational Inference (AVI). Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words. This work includes enhancement of the previous AVI architecture and also modification of the loss function. Ultimately, we present the Autoencoding Variational Inference for Joint Sentiment/Topic (AVIJST) model. In this model, we substantially extend the AVI model to support the JST model, which performs topic modeling for corresponding sentiment. The experimental results show that our proposed models enjoy higher topic coherent, faster convergence time and better accuracy on sentiment classification, as compared to their LDA-based counterparts.

http://arxiv.org/abs/1902.02507v3

cs.CL, cs.LG

Semantic and Influence aware k-Representative Queries over Social Streams

Yanhao Wang, Yuchen Li, Kian-Lee Tan

Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is \emph{social search}. Although there have been extensive studies on social search, existing methods only focus on the \emph{relevance} of query results but ignore the \emph{representativeness}. In this paper, we propose a novel Semantic and Influence aware $k$-Representative ($k$-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A $k$-SIR query retrieves a set of $k$ elements with the maximum \emph{representativeness} over the sliding window at query time w.r.t. the query vector. The representativeness of an element set comprises both semantic and influence scores computed by the topic model. Subsequently, we design two approximation algorithms, namely \textsc{Multi-Topic ThresholdStream} (MTTS) and \textsc{Multi-Topic ThresholdDescend} (MTTD), to process $k$-SIR queries in real-time. Both algorithms leverage the ranked lists maintained on each topic for $k$-SIR processing with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness of $k$-SIR query compared with existing methods as well as the efficiency and scalability of our proposed algorithms for $k$-SIR processing.

http://arxiv.org/abs/1901.10109v1

cs.SI, cs.DB

A new evaluation framework for topic modeling algorithms based on synthetic corpora

Hanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis A. N. Amaral

Topic models are in widespread use in natural language processing and beyond. Here, we propose a new framework for the evaluation of probabilistic topic modeling algorithms based on synthetic corpora containing an unambiguously defined ground truth topic structure. The major innovation of our approach is the ability to quantify the agreement between the planted and inferred topic structures by comparing the assigned topic labels at the level of the tokens. In experiments, our approach yields novel insights about the relative strengths of topic models as corpus characteristics vary, and the first evidence of an "undetectable phase" for topic models when the planted structure is weak. We also establish the practical relevance of the insights gained for synthetic corpora by predicting the performance of topic modeling algorithms in classification tasks in real-world corpora.

http://arxiv.org/abs/1901.09848v1

cs.CL, cs.LG, physics.soc-ph

Measuring national capability over big sciences multidisciplinarity: A case study of nuclear fusion research

Hyunuk Kim, Inho Hong, Woo-Sung Jung

In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the "tokamak" is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Here, to explore the scientific effects of tokamaks, we map a country's research capability in nuclear fusion research with normalized revealed comparative advantage on five topical clusters -- material, plasma, device, diagnostics, and simulation -- detected through a dynamic topic model. Our approach captures not only the growth of China, India, and the Republic of Korea but also the decline of Canada, Japan, Sweden, and the Netherlands. Time points of their rise and fall are related to tokamak operation, highlighting the importance of large facilities in big science. The gravity model points out that two countries collaborate less in device, diagnostics, and plasma research if they have comparative advantages in different topics. This relation is a unique feature of nuclear fusion compared to other science fields. Our results can be used and extended when building national policies for big science.

http://arxiv.org/abs/1901.09099v1

cs.DL, physics.plasm-ph, physics.soc-ph

PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography

Maksim Tsikhanovich, Malik Magdon-Ismail, Muhammad Ishaq, Vassilis Zikas

Privacy is a major issue in learning from distributed data. Recently the cryptographic literature has provided several tools for this task. However, these tools either reduce the quality/accuracy of the learning algorithm---e.g., by adding noise---or they incur a high performance penalty and/or involve trusting external authorities. We propose a methodology for {\sl private distributed machine learning from light-weight cryptography} (in short, PD-ML-Lite). We apply our methodology to two major ML algorithms, namely non-negative matrix factorization (NMF) and singular value decomposition (SVD). Our resulting protocols are communication optimal, achieve the same accuracy as their non-private counterparts, and satisfy a notion of privacy---which we define---that is both intuitive and measurable. Our approach is to use lightweight cryptographic protocols (secure sum and normalized secure sum) to build learning algorithms rather than wrap complex learning algorithms in a heavy-cost MPC framework. We showcase our algorithms' utility and privacy on several applications: for NMF we consider topic modeling and recommender systems, and for SVD, principal component regression, and low rank approximation.

http://arxiv.org/abs/1901.07986v2

cs.LG, cs.CR, stat.ML

Dirichlet Variational Autoencoder

Weonyoung Joo, Wonsung Lee, Sungrae Park, Il-Chul Moon

This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabilities. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the Gamma distribution, which is a component of the Dirichlet distribution, with the inverse Gamma CDF approximation. Additionally, we reshape the component collapsing issue by investigating two problem sources, which are decoder weight collapsing and latent value collapsing, and we show that DirVAE has no component collapsing; while Gaussian VAE exhibits the decoder weight collapsing and Stick-Breaking VAE shows the latent value collapsing. The experimental results show that 1) DirVAE models the latent representation result with the best log-likelihood compared to the baselines; and 2) DirVAE produces more interpretable latent values with no collapsing issues which the baseline models suffer from. Also, we show that the learned latent representation from the DirVAE achieves the best classification accuracy in the semi-supervised and the supervised classification tasks on MNIST, OMNIGLOT, and SVHN compared to the baseline VAEs. Finally, we demonstrated that the DirVAE augmented topic models show better performances in most cases.

http://arxiv.org/abs/1901.02739v1

cs.LG, stat.ML

Learning Nonlinear Mixtures: Identifiability and Algorithm

Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Kejun Huang

Linear mixture models have proven very useful in a plethora of applications, e.g., topic modeling, clustering, and source separation. As a critical aspect of the linear mixture models, identifiability of the model parameters is well-studied, under frameworks such as independent component analysis and constrained matrix factorization. Nevertheless, when the linear mixtures are distorted by an unknown nonlinear functions -- which is well-motivated and more realistic in many cases -- the identifiability issues are much less studied. This work proposes an identification criterion for a nonlinear mixture model that is well grounded in many real-world applications, and offers identifiability guarantees. A practical implementation based on a judiciously designed neural network is proposed to realize the criterion, and an effective learning algorithm is proposed. Numerical results on synthetic and real-data corroborate effectiveness of the proposed method.

http://arxiv.org/abs/1901.01568v1

cs.LG, stat.ML

Mixed Membership Recurrent Neural Networks

Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres, Kevin Wu, John Paisley

Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for group-level effects when multiple sequences are observed. We propose a model for grouped sequential data based on the RNN that accounts for varying time intervals between observations in a sequence by learning a group-level base parameter to which each sequence can revert. Our approach is motivated by the mixed membership framework, and we show how it can be used for dynamic topic modeling in which the distribution on topics (not the topics themselves) are evolving in time. We demonstrate our approach on a dataset of 3.4 million online grocery shopping orders made by 206K customers.

http://arxiv.org/abs/1812.09645v1

cs.LG, stat.ML

Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences

Hamed Jelodar, Yongli Wang, Mahdi Rabbani, Ru-xin Zhao, Seyedvalyallah Ayobi, Peng Hu, Isma Masood

Recommendation systems have an important place to help online users in the internet society. Recommendation Systems in computer science are of very practical use these days in various aspects of the Internet portals, such as social networks, and library websites. There are several approaches to implement recommendation systems, Latent Dirichlet Allocation (LDA) is one the popular techniques in Topic Modeling. Recently, researchers have proposed many approaches based on Recommendation Systems and LDA. According to importance of the subject, in this paper we discover the trends of the topics and find relationship between LDA topics and Scholar-Context-documents. In fact, We apply probabilistic topic modeling based on Gibbs sampling algorithms for a semantic mining from six conference publications in computer science from DBLP dataset. According to our experimental results, our semantic framework can be effective to help organizations to better organize these conferences and cover future research topics.

http://arxiv.org/abs/1812.08304v1

cs.IR, cs.CL

Structured Neural Topic Models for Reviews

Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent

We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for combined reviews associated with each paired user and item onto structured embeddings, which in turn define per-aspect topic weights. We model individual reviews in a structured manner by inferring an aspect assignment for each sentence in a given review, where the per-aspect topic weights obtained by the user-item encoder serve to define a mixture over topics, conditioned on the aspect. The result is an autoencoding neural topic model for reviews, which can be trained in a fully unsupervised manner to learn topics that are structured into aspects. Experimental evaluation on large number of datasets demonstrates that aspects are interpretable, yield higher coherence scores than non-structured autoencoding topic model variants, and can be utilized to perform aspect-based comparison and genre discovery.

http://arxiv.org/abs/1812.05035v2

cs.CL, cs.LG

An Exploratory Study of (#)Exercise in the Twittersphere

George Shaw, Amir Karami

Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. The most discussed exercise-related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). The results from our experiment indicate that the exploratory data analysis is a practical approach to summarizing the various characteristics of text data for different health and medical applications.

http://arxiv.org/abs/1812.03260v1

stat.AP, cs.CL, cs.CY, stat.ML

Learning Representations of Social Media Users

Adrian Benton

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.

http://arxiv.org/abs/1812.00436v1

cs.LG, cs.CL

Naive Dictionary On Musical Corpora: From Knowledge Representation To Pattern Recognition

Qiuyi Wu, Ernest Fokoue

In this paper, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modelling. We specifically introduce the idea of representing any given piece of music as a collection of "musical words" that we codenamed "muselets", which are essentially musical words of various lengths. Given the novelty and therefore the extremely difficulty of properly forming a complete version of a dictionary of muselets, the present paper focuses on a simpler albeit naive version of the ultimate dictionary, which we refer to as a Naive Dictionary because of the fact that all the words are of the same length. We specifically herein construct a naive dictionary featuring a corpus made up of African American, Chinese, Japanese and Arabic music, on which we perform both topic modelling and pattern recognition. Although some of the results based on the Naive Dictionary are reasonably good, we anticipate phenomenal predictive performances once we get around to actually building a full scale complete version of our intended dictionary of muselets.

http://arxiv.org/abs/1811.12802v1

cs.IR, cs.LG, cs.SD, eess.AS, stat.ML, 62P15, 62P25, 62P99, 68W40, 68W01, 91E10, 91E45, 82-08, 62-07, E.2; F.1.1; F.2.0; I.1.3; I.1.4; I.2.4; I.2.1; I.2.6; I.5.5; I.7.0

Towards Large-Scale Exploratory Search over Heterogeneous Sources

Mariia Seleznova, Anton Belyy, Aleksei Sholokhov

Since time immemorial, people have been looking for ways to organize scientific knowledge into some systems to facilitate search and discovery of new ideas. The problem was partially solved in the pre-Internet era using library classifications, but nowadays it is nearly impossible to classify all scientific and popular scientific knowledge manually. There is a clear gap between the diversity and the amount of data available on the Internet and the algorithms for automatic structuring of such data. In our preliminary study, we approach the problem of knowledge discovery on web-scale data with diverse text sources and propose an algorithm to aggregate multiple collections into a single hierarchical topic model. We implement a web service named Rysearch to demonstrate the concept of topical exploratory search and make it available online.

http://arxiv.org/abs/1811.07042v2

cs.IR, cs.LG, stat.ML

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

Miaoyan Wang, Lexin Li

We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based estimation method, and obtain the theoretical accuracy guarantees. In contrast to continuous-valued problems, the binary tensor problem exhibits an interesting phase transition phenomenon according to the signal-to-noise ratio. The error bound for the parameter tensor estimation is established, and we show that the obtained rate is minimax optimal under the considered model. Furthermore, we develop an alternating optimization algorithm with convergence guarantees. The efficacy of our approach is demonstrated through both simulations and analyses of multiple data sets on the tasks of tensor completion and clustering.

http://arxiv.org/abs/1811.05076v3

stat.ML, cs.LG, math.ST, stat.ME, stat.TH, 62H25, 65F35, 62F12

MMALFM: Explainable Recommendation by Leveraging Reviews and Images

Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, Mohan Kankanhalli

Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this paper, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users' preferences and items' features from different aspects, and also estimate the aspect importance of a user towards an item. Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user's preferences and the targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets to demonstrate the effectiveness of our method.

http://arxiv.org/abs/1811.05318v2

cs.IR

Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders

Jen J. Gong, Abigail Z. Jacobs, Toby E. Stuart, Mathijs de Vaan

The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed with opioid use disorder, have diverse underlying health states. Policy interventions targeting prescription opioid use, opioid use disorder, and overdose often fail to account for this variation. To identify latent health states, or phenotypes, pertinent to opioid use and opioid use disorders, we use probabilistic topic modeling with medical diagnosis histories from a statewide population of individuals who were prescribed opioids. We demonstrate that our learned phenotypes are predictive of future opioid use-related outcomes. In addition, we show how the learned phenotypes can provide important context for variability in opioid prescriptions. Understanding the heterogeneity in individual health states and in prescription opioid use can help identify policy interventions to address this public health crisis.

http://arxiv.org/abs/1811.04344v3

q-bio.QM, cs.LG, stat.ML

Construction and Quality Evaluation of Heterogeneous Hierarchical Topic Models

Anton Belyy

In our work, we propose to represent HTM as a set of flat models, or layers, and a set of topical hierarchies, or edges. We suggest several quality measures for edges of hierarchical models, resembling those proposed for flat models. We conduct an assessment experimentation and show strong correlation between the proposed measures and human judgement on topical edge quality. We also introduce heterogeneous algorithm to build hierarchical topic models for heterogeneous data sources. We show how making certain adjustments to learning process helps to retain original structure of customized models while allowing for slight coherent modifications for new documents. We evaluate this approach using the proposed measures and show that the proposed heterogeneous algorithm significantly outperforms the baseline concat approach. Finally, we implement our own ESE called Rysearch, which demonstrates the potential of ARTM approach for visualizing large heterogeneous document collections.

http://arxiv.org/abs/1811.02820v1

cs.IR, cs.LG, stat.ML

DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora

Robert Giaquinto, Arindam Banerjee

Extracting common narratives from multi-author dynamic text corpora requires complex models, such as the Dynamic Author Persona (DAP) topic model. However, such models are complex and can struggle to scale to large corpora, often because of challenging non-conjugate terms. To overcome such challenges, in this paper we adapt new ideas in approximate inference to the DAP model, resulting in the DAP Performed Exceedingly Rapidly (DAPPER) topic model. Specifically, we develop Conjugate-Computation Variational Inference (CVI) based variational Expectation-Maximization (EM) for learning the model, yielding fast, closed form updates for each document, replacing iterative optimization in earlier work. Our results show significant improvements in model fit and training time without needing to compromise the model's temporal structure or the application of Regularized Variation Inference (RVI). We demonstrate the scalability and effectiveness of the DAPPER model by extracting health journeys from the CaringBridge corpus --- a collection of 9 million journals written by 200,000 authors during health crises.

http://arxiv.org/abs/1811.01931v1

stat.ML, cs.CL, cs.IR, cs.LG

Dirichlet belief networks for topic structure learning

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.

http://arxiv.org/abs/1811.00717v1

cs.IR, cs.CL, cs.LG, stat.ML

A latent topic model for mining heterogenous non-randomly missing electronic health records data

Yue Li, Manolis Kellis

Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised generative model integrating collaborative filtering and latent topic models, which jointly models the discrete distributions of data observation bias and actual data using latent disease-topic distributions. We apply mixEHR on 12.8 million phenotypic observations from the MIMIC dataset, and use it to reveal latent disease topics, interpret EHR results, impute missing data, and predict mortality in intensive care units. Using both simulation and real data, we show that mixEHR outperforms previous methods and reveals meaningful multi-disease insights.

http://arxiv.org/abs/1811.00464v1

cs.LG, stat.ML

ATM:Adversarial-neural Topic Model

Rui Wang, Deyu Zhou, Yulan He

Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address these limitations, we propose a topic modeling approach based on Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM). The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. To illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. Our experimental results on the two public corpora show that ATM generates more coherence topics, outperforming a number of competitive baselines. Moreover, ATM is able to extract meaningful events from news articles.

http://arxiv.org/abs/1811.00265v2

cs.AI

Topic representation: finding more representative words in topic models

Jinjin Chi, Jihong Ouyang, Changchun Li, Xueyang Dong, Ximing Li, Xinhua Wang

The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list are not always representative. The objective of this paper is to find more representative top word lists for topics. To achieve this, we rerank the words in a given topic by further considering marginal probability on words over every other topic. The reranking list of top-M words is used to be a novel topic representation for topic models. We investigate three reranking methodologies, using (1) standard deviation weight, (2) standard deviation weight with topic size and (3) Chi Square \c{hi}2statistic selection. Experimental results on real world collections indicate that our representations can extract more representative words for topics, agreeing with human judgements.

http://arxiv.org/abs/1810.10307v1

cs.IR, cs.LG, stat.ML

Conceptual Organization is Revealed by Consumer Activity Patterns

Adam N. Hornsby, Thomas Evans, Peter Riefer, Rosie Prior, Bradley C. Love

Meaning may arise from an element's role or interactions within a larger system. For example, hitting nails is more central to people's concept of a hammer than its particular material composition or other intrinsic features. Likewise, the importance of a web page may result from its links with other pages rather than solely from its content. One example of meaning arising from extrinsic relationships are approaches that extract the meaning of word concepts from co-occurrence patterns in large, text corpora. The success of these methods suggest that human activity patterns may reveal conceptual organization. However, texts do not directly reflect human activity, but instead serve a communicative function and are usually highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns, namely supermarket purchases. Using product co-occurrence data from nearly 1.3m shopping baskets, we trained a topic model to learn 25 high-level concepts (or "topics"). These topics were found to be comprehensible and coherent by both retail experts and consumers. Topics ranged from specific (e.g., ingredients for a stir-fry) to general (e.g., cooking from scratch). Topics tended to be goal-directed and situational, consistent with the notion that human conceptual knowledge is tailored to support action. Individual differences in the topics sampled predicted basic demographic characteristics. These results suggest that human activity patterns reveal conceptual organization and may give rise to it.

http://arxiv.org/abs/1810.08577v1

cs.AI, cs.CL

Contextual Topic Modeling For Dialog Systems

Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Angeliki Metanillou, Anushree Venkatesh, Raefer Gabriel, Arindam Mandal

Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems. In this work, we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs. We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features. On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances. We show that topical metrics such as topical depth is highly correlated with dialog evaluation metrics such as coherence and engagement implying that conversational topic models can predict user satisfaction. Our work for detecting conversation topics and keywords can be used to guide chatbots towards coherent dialog.

http://arxiv.org/abs/1810.08135v2

cs.CL

Hierarchical Methods of Moments

Matteo Ruffini, Guillaume Rabusseau, Borja Balle

Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.

http://arxiv.org/abs/1810.07468v1

stat.ML, cs.LG

Spherical Triangle Algorithm: A Fast Oracle for Convex Hull Membership Queries

Bahman Kalantari, Yikai Zhang

The it Convex Hull Membership(CHM) problem is: Given a point $p$ and a subset $S$ of $n$ points in $\mathbb{R}^m$, is $p \in conv(S)$? CHM is not only a fundamental problem in Linear Programming, Computational Geometry, Machine Learning and Statistics, it also serves as a query problem in many applications e.g. Topic Modeling, LP Feasibility, Data Reduction. The {\it Triangle Algorithm} (TA) \cite{kalantari2015characterization} either computes an approximate solution in the convex hull, or a separating hyperplane. The {\it Spherical}-CHM is a CHM, where $p=0$ and each point in $S$ has unit norm. First, we prove the equivalence of exact and approximate versions of CHM and Spherical-CHM. On the one hand, this makes it possible to state a simple version of the original TA. On the other hand, we prove that under the satisfiability of a simple condition in each iteration, the complexity improves to $O(1/\varepsilon)$. The analysis also suggests a strategy for when the property does not hold at an iterate. This suggests the \textit{Spherical-TA} which first converts a given CHM into a Spherical-CHM before applying the algorithm. Next we introduce a series of applications of Spherical-TA. In particular, Spherical-TA serves as a fast version of vanilla TA to boost its efficiency. As an example, this results in a fast version of \emph{AVTA} \cite{awasthi2018robust}, called \emph{AVTA$^+$} for solving exact or approximate irredundancy problem. Computationally, we have considered CHM, LP and Strict LP Feasibility and the Irredundancy problem. Based on substantial amount of computing, Spherical-TA achieves better efficiency than state of the art algorithms. Leveraging on the efficiency of Spherical-TA, we propose AVTA$^+$ as a pre-processing step for data reduction which arises in such applications as in computing the Minimum Volume Enclosing Ellipsoid \cite{moshtagh2005minimum}.

http://arxiv.org/abs/1810.07346v3

cs.CG, 90C05, 90C25, 65D18, 32C37, G.1.6; I.3.5; F.2.1

Can Euroscepticism Contribute to a European Public Sphere? The Europeanization of Media Discourses about Euroscepticism across Six Countries

Anamaria Dutceac Segesten, Michael Bossetta

This study compares the media discourses about Euroscepticism in 2014 across six countries (United Kingdom, Ireland, France, Spain, Sweden, and Denmark). We assess the extent to which the mass media's reporting of Euroscepticism indicates the Europeanization of public spheres. Using a mixed-methods approach combining LDA topic modeling and qualitative coding, we find that approximately 70 per cent of print articles mentioning "Euroscepticism" or "Eurosceptic" are framed in a non-domestic (i.e. European) context. In five of the six cases studied, articles exhibiting a European context are strikingly similar in content, with the British case as the exception. However, coverage of British Euroscepticism drives Europeanization in other Member States. Bivariate logistic regressions further reveal three macro-level structural variables that significantly correlate with a Europeanized media discourse: newspaper type (tabloid or broadsheet), presence of a strong Eurosceptic party, and relationship to the EU budget (net contributor or receiver of EU funds).

http://arxiv.org/abs/1810.06745v1

cs.CL, cs.CY

Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics

Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick

Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks. However, due to the flexibility of these models, the consequences of prior choices can be opaque. And so prior specification can be relatively difficult. At the same time, prior choice can have a substantial effect on posterior inferences. Thus, considerations of robustness need to go hand in hand with nonparametric modeling. In the current paper, we tackle this challenge by exploiting the fact that variational Bayesian methods, in addition to having computational advantages in fitting complex nonparametric models, also yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models. In particular, we demonstrate how to assess the sensitivity of conclusions to the choice of concentration parameter and stick-breaking distribution for inferences under Dirichlet process mixtures and related mixture models. We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.

http://arxiv.org/abs/1810.06587v3

stat.ME

Improving Topic Models with Latent Feature Word Representations

Dat Quoc Nguyen, Richard Billingsley, Lan Du, Mark Johnson

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

http://arxiv.org/abs/1810.06306v1

cs.CL, cs.IR, cs.LG

An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models

Shudong Hao, Michael J. Paul

Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual topic models have been developed, their assumptions on the training corpus are quite varied, and it is not clear how well the models can be applied under various training conditions. In this paper, we systematically study the knowledge transfer mechanisms behind different multilingual topic models, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.

http://arxiv.org/abs/1810.05867v2

cs.CL

She's Reddit: A source of statistically significant gendered interest information?

Mike Thelwall, Emma Stuart

Information about gender differences in interests is necessary to disentangle the effects of discrimination and choice when gender inequalities occur, such as in employment. This article assesses gender differences in interests within the popular social news and entertainment site Reddit. A method to detect terms that are statistically significantly used more by males or females in 181 million comments in 100 subreddits shows that gender affects both the selection of subreddits and activities within most of them. The method avoids the hidden gender biases of topic modelling for this task. Although the method reveals statistically significant gender differences in interests for topics that are extensively discussed on Reddit, it cannot give definitive causes, and imitation and sharing within the site mean that additional checking is needed to verify the results. Nevertheless, with care, Reddit can serve as a useful source of insights into gender differences in interests.

http://arxiv.org/abs/1810.08091v1

cs.CY, cs.DL

Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan

Mirco Schoenfeld, Steffen Eckhard, Ronny Patz, Hilde van Meegdenburg

The recent turn towards quantitative text-as-data approaches in IR brought new ways to study the discursive landscape of world politics. Here seen as complementary to qualitative approaches, quantitative assessments have the advantage of being able to order and make comprehensible vast amounts of text. However, the validity of unsupervised methods applied to the types of text available in large quantities needs to be established before they can speak to other studies relying on text and discourse as data. In this paper, we introduce a new text corpus of United Nations Security Council (UNSC) speeches on Afghanistan between 2001 and 2017; we study this corpus through unsupervised topic modeling (LDA) with the central aim to validate the topic categories that the LDA identifies; and we discuss the added value, and complementarity, of quantitative text-as-data approaches. We set-up two tests using mixed- method approaches. Firstly, we evaluate the identified topics by assessing whether they conform with previous qualitative work on the development of the situation in Afghanistan. Secondly, we use network analysis to study the underlying social structures of what we will call 'speaker-topic relations' to see whether they correspondent to know divisions and coalitions in the UNSC. In both cases we find that the unsupervised LDA indeed provides valid and valuable outputs. In addition, the mixed-method approaches themselves reveal interesting patterns deserving future qualitative research. Amongst these are the coalition and dynamics around the 'women and human rights' topic as part of the UNSC debates on Afghanistan.

http://arxiv.org/abs/1810.05572v1

cs.SI

HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents

Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter, Maarten Marx, Jaap Kamps, Maarten de Rijke

A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents. Topic models play a central role in this approach and, hence, their quality is crucial to the efficacy of measuring topical diversity. The quality of topic models is affected by two causes: generality and impurity of topics. General topics only include common information of a background corpus and are assigned to most of the documents. Impure topics contain words that are not related to the topic. Impurity lowers the interpretability of topic models. Impure topics are likely to get assigned to documents erroneously. We propose a hierarchical re-estimation process aimed at removing generality and impurity. Our approach has three re-estimation components: (1) document re-estimation, which removes general words from the documents; (2) topic re-estimation, which re-estimates the distribution over words of each topic; and (3) topic assignment re-estimation, which re-estimates for each document its distributions over topics. For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.

http://arxiv.org/abs/1810.05436v1

cs.CL, cs.IR, cs.LG

textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior

Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze

We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost. The LSTM-LM learns a vector-space representation of each word by accounting for word order in local collocation patterns and models complex characteristics of language (e.g., syntax and semantics), while the TM simultaneously learns a latent representation from the entire document and discovers the underlying thematic structure. We unite two complementary paradigms of learning the meaning of word occurrences by combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework, named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of documents: In settings with a small number of word occurrences (i.e., lack of context) in short text or data sparsity in a corpus of few documents, the application of TMs is challenging. We address this challenge by incorporating external knowledge into neural autoregressive topic models via a language modelling approach: we use word embeddings as input of a LSTM-LM with the aim to improve the word-topic mapping on a smaller and/or short-text corpus. The proposed DocNADE extension is named as ctx-DocNADEe. We present novel neural autoregressive topic model variants coupled with neural LMs and embeddings priors that consistently outperform state-of-the-art generative TMs in terms of generalization (perplexity), interpretability (topic coherence) and applicability (retrieval and classification) over 6 long-text and 8 short-text datasets from diverse domains.

http://arxiv.org/abs/1810.03947v4

cs.CL, cs.AI, cs.IR, cs.LG

Clust-LDA: Joint Model for Text Mining and Author Group Inference

Shaoyang Ning, Xi Qu, Victor Cai, Nathan Sanders

Social media corpora pose unique challenges and opportunities, including typically short document lengths and rich meta-data such as author characteristics and relationships. This creates great potential for systematic analysis of the enormous body of the users and thus provides implications for industrial strategies such as targeted marketing. Here we propose a novel and statistically principled method, clust-LDA, which incorporates authorship structure into the topical modeling, thus accomplishing the task of the topical inferences across documents on the basis of authorship and, simultaneously, the identification of groupings between authors. We develop an inference procedure for clust-LDA and demonstrate its performance on simulated data, showing that clust-LDA out-performs the "vanilla" LDA on the topic identification task where authors exhibit distinctive topical preference. We also showcase the empirical performance of clust-LDA based on a real-world social media dataset from Reddit.

http://arxiv.org/abs/1810.02717v1

cs.IR, cs.SI, stat.AP

Semantic Topic Analysis of Traffic Camera Images

Jeffrey Liu, Andrew Weinert, Saurabh Amin

Traffic cameras are commonly deployed monitoring components in road infrastructure networks, providing operators visual information about conditions at critical points in the network. However, human observers are often limited in their ability to process simultaneous information sources. Recent advancements in computer vision, driven by deep learning methods, have enabled general object recognition, unlocking opportunities for camera-based sensing beyond the existing human observer paradigm. In this paper, we present a Natural Language Processing (NLP)-inspired approach, entitled Bag-of-Label-Words (BoLW), for analyzing image data sets using exclusively textual labels. The BoLW model represents the data in a conventional matrix form, enabling data compression and decomposition techniques, while preserving semantic interpretability. We apply the Latent Dirichlet Allocation (LDA) topic model to decompose the label data into a small number of semantic topics. To illustrate our approach, we use freeway camera images collected from the Boston area between December 2017-January 2018. We analyze the cameras' sensitivity to weather events; identify temporal traffic patterns; and analyze the impact of infrequent events, such as the winter holidays and the "bomb cyclone" winter storm. This study demonstrates the flexibility of our approach, which allows us to analyze weather events and freeway traffic using only traffic camera image labels.

http://arxiv.org/abs/1809.10707v1

cs.CV

Text Similarity in Vector Space Models: A Comparative Study

Omid Shahmirzadi, Adam Lugowski, Kenneth Younge

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.

http://arxiv.org/abs/1810.00664v1

cs.CL, cs.LG, stat.ML

Scalable inference of topic evolution via models for latent geometric structures

Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.

http://arxiv.org/abs/1809.08738v3

stat.ML, cs.CL, cs.LG

Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization

Ancil Crayton

Communication is now a standard tool in the central bank's monetary policy toolkit. Theoretically, communication provides the central bank an opportunity to guide public expectations, and it has been shown empirically that central bank communication can lead to financial market fluctuations. However, there has been little research into which dimensions or topics of information are most important in causing these fluctuations. We develop a semi-automatic methodology that summarizes the FOMC statements into its main themes, automatically selects the best model based on coherency, and assesses whether there is a significant impact of these themes on the shape of the U.S Treasury yield curve using topic modeling methods from the machine learning literature. Our findings suggest that the FOMC statements can be decomposed into three topics: (i) information related to the economic conditions and the mandates, (ii) information related to monetary policy tools and intermediate targets, and (iii) information related to financial markets and the financial crisis. We find that statements are most influential during the financial crisis and the effects are mostly present in the curvature of the yield curve through information related to the financial theme.

http://arxiv.org/abs/1809.08718v1

econ.GN, cs.CL, q-fin.EC

Modeling Online Discourse with Coupled Distributed Topics

Nikita Srivatsan, Zachary Wojtowicz, Taylor Berg-Kirkpatrick

In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.

http://arxiv.org/abs/1809.07282v3

cs.LG, cs.CL, stat.ML

In-Session Personalization for Talent Search

Sahin Cem Geyik, Vijay Dialani, Meng Meng, Ryan Smith

Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model. Traditionally, the generated recommendations are final, that is, the list of potential candidates is not modified unless the user explicitly changes his/her search criteria. In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step. This setting also allows for very uninformative initial search queries, since we pinpoint the user's intent due to the feedback during the search session. To achieve our goal, we employ an intent clustering method based on topic modeling which separates the candidate space into meaningful, possibly overlapping, subsets (which we call intent clusters) for each position. On top of the candidate segments, we apply a multi-armed bandit approach to choose which intent cluster is more appropriate for the current session. We also present an online learning scheme which updates the intent clusters within the session, due to user feedback, to achieve further personalization. Our offline experiments as well as the results from the online deployment of our solution demonstrate the benefits of our proposed methodology.

http://arxiv.org/abs/1809.06488v1

cs.AI

Cross-Domain Labeled LDA for Cross-Domain Text Classification

Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.

http://arxiv.org/abs/1809.05820v1

cs.CL

Document Informed Neural Autoregressive Topic Models with Distributional Prior

Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze

We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts "artificial neural networks" vs. "biological neuron networks". Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion. The proposed model is named as iDocNADE. (2) Due to the small number of word occurrences (i.e., lack of context) in short text and data sparsity in a corpus of few documents, the application of topic models is challenging on such texts. Therefore, we propose a simple and efficient way of incorporating external knowledge into neural autoregressive topic models: we use embeddings as a distributional prior. The proposed variants are named as DocNADEe and iDocNADEe. We present novel neural autoregressive topic model variants that consistently outperform state-of-the-art generative topic models in terms of generalization, interpretability (topic coherence) and applicability (retrieval and classification) over 7 long-text and 8 short-text datasets from diverse domains.

http://arxiv.org/abs/1809.06709v2

cs.CL, cs.AI, cs.IR, cs.LG

Unsupervised Machine Commenting with Neural Variational Topic Model

Shuming Ma, Lei Cui, Furu Wei, Xu Sun

Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic commenting system requires large parallel corpora. Although part of articles are naturally paired with the comments on some websites, most articles and comments are unpaired on the Internet. To fully exploit the unpaired data, we completely remove the need for parallel data and propose a novel unsupervised approach to train an automatic article commenting model, relying on nothing but unpaired articles and comments. Our model is based on a retrieval-based commenting framework, which uses news to retrieve comments based on the similarity of their topics. The topic representation is obtained from a neural variational topic model, which is trained in an unsupervised manner. We evaluate our model on a news comment dataset. Experiments show that our proposed topic-based approach significantly outperforms previous lexicon-based models. The model also profits from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios.

http://arxiv.org/abs/1809.04960v1

cs.CL

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin

We propose a novel Wasserstein method with a distillation mechanism, yielding joint learning of word embeddings and topics. The proposed method is based on the fact that the Euclidean distance between word embeddings may be employed as the underlying distance in the Wasserstein topic model. The word distributions of topics, their optimal transports to the word distributions of documents, and the embeddings of words are learned in a unified framework. When learning the topic model, we leverage a distilled underlying distance matrix to update the topic distributions and smoothly calculate the corresponding optimal transports. Such a strategy provides the updating of word embeddings with robust guidance, improving the algorithmic convergence. As an application, we focus on patient admission records, in which the proposed method embeds the codes of diseases and procedures and learns the topics of admissions, obtaining superior performance on clinically-meaningful disease network construction, mortality prediction as a function of admission codes, and procedure recommendation.

http://arxiv.org/abs/1809.04705v1

cs.LG, cs.CL, stat.ML

A Joint Model of Conversational Discourse and Latent Topics on Microblogs

Jing Li, Yan Song, Zhongyu Wei, Kam-Fai Wong

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.

http://arxiv.org/abs/1809.03690v1

cs.CL

Coherence-Aware Neural Topic Modeling

Ran Ding, Ramesh Nallapati, Bing Xiang

Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.

http://arxiv.org/abs/1809.02687v1

cs.CL, cs.LG

An operational definition of quark and gluon jets

Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler

While "quark" and "gluon" jets are often treated as separate, well-defined objects in both theoretical and experimental contexts, no precise, practical, and hadron-level definition of jet flavor presently exists. To remedy this issue, we develop and advocate for a data-driven, operational definition of quark and gluon jets that is readily applicable at colliders. Rather than specifying a per-jet flavor label, we aggregately define quark and gluon jets at the distribution level in terms of measured hadronic cross sections. Intuitively, quark and gluon jets emerge as the two maximally separable categories within two jet samples in data. Benefiting from recent work on data-driven classifiers and topic modeling for jets, we show that the practical tools needed to implement our definition already exist for experimental applications. As an informative example, we demonstrate the power of our operational definition using Z+jet and dijet samples, illustrating that pure quark and gluon distributions and fractions can be successfully extracted in a fully well-defined manner.

http://arxiv.org/abs/1809.01140v2

hep-ph, hep-ex

Measuring LDA Topic Stability from Clusters of Replicated Runs

Mika Mäntylä, Maëlick Claes, Umar Farooq

Background: Unstructured and textual data is increasing rapidly and Latent Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods for it. Past work suggests that instability of LDA topics may lead to systematic errors. Aim: We propose a method that relies on replicated LDA runs, clustering, and providing a stability metric for the topics. Method: We generate k LDA topics and replicate this process n times resulting in n*k topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k clusters now represent the original LDA topics and we present them like normal LDA topics showing the ten most probable words. For the clusters, we try multiple stability metrics, out of which we recommend Rank-Biased Overlap, showing the stability of the topics inside the clusters. Results: We provide an initial validation where our method is used for 270,000 Mozilla Firefox commit messages with k=20 and n=20. We show how our topic stability metrics are related to the contents of the topics. Conclusions: Advances in text mining enable us to analyze large masses of text in software engineering but non-deterministic algorithms, such as LDA, may lead to unreplicable conclusions. Our approach makes LDA stability transparent and is also complementary rather than alternative to many prior works that focus on LDA parameter tuning.

http://arxiv.org/abs/1808.08098v1

cs.CL

Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process

Peiyuan Suny, Jianxin Li, Yongyi Mao, Richong Zhang, Lihong Wang

Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of events like "post" or "repost" associated with each node, and (2) the interactions between nodes encompass multiple distinct patterns which in turn affect the diffusion patterns. For instance, social interactions seldom develop on a single channel, and multiple relationships can bind pairs of people due to their various common interests. Most previous work considers only one of these two challenges which is apparently unrealistic. In this paper, we study the problem of \emph{inferring multiplex network} in social networks. We propose the Multiplex Diffusion Model (MDM) which incorporates the multivariate marked Hawkes process and topic model to infer the multiplex structure of social network. A MCMC based algorithm is developed to infer the latent multiplex structure and to estimate the node-related parameters. We evaluate our model based on both synthetic and real-world datasets. The results show that our model is more effective in terms of uncovering the multiplex network structure.

http://arxiv.org/abs/1809.07688v1

cs.SI, cs.LG, stat.ML

What do the US West Coast Public Libraries Post on Twitter?

Amir Karami, Matthew Collins

Twitter has provided a great opportunity for public libraries to disseminate information for a variety of purposes. Twitter data have been applied in different domains such as health, politics, and history. There are thousands of public libraries in the US, but no study has yet investigated the content of their social media posts like tweets to find their interests. Moreover, traditional content analysis of Twitter content is not an efficient task for exploring thousands of tweets. Therefore, there is a need for automatic methods to overcome the limitations of manual methods. This paper proposes a computational approach to collecting and analyzing using Twitter Application Programming Interfaces (API) and investigates more than 138,000 tweets from 48 US west coast libraries using topic modeling. We found 20 topics and assigned them to five categories including public relations, book, event, training, and social good. Our results show that the US west coast libraries are more interested in using Twitter for public relations and book-related events. This research has both practical and theoretical applications for libraries as well as other organizations to explore social media actives of their customer and themselves.

http://arxiv.org/abs/1808.06021v2

cs.CY, cs.CL, stat.AP, stat.ML

Learning Supervised Topic Models for Classification and Regression from Crowds

Filipe Rodrigues, Mariana Lourenço, Bernardete Ribeiro, Francisco Pereira

The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

http://arxiv.org/abs/1808.05902v1

stat.ML, cs.CL, cs.CV, cs.HC, cs.LG

jLDADMM: A Java package for the LDA and DMM topic models

Dat Quoc Nguyen

In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. jLDADMM is open-source and available to download at: https://github.com/datquocnguyen/jLDADMM

http://arxiv.org/abs/1808.03835v1

cs.IR, cs.CL, cs.LG, stat.ML

Document Informed Neural Autoregressive Topic Models

Pankaj Gupta, Florian Buettner, Hinrich Schütze

Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks. Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion. This results in an improved performance in terms of generalization, interpretability and applicability. We apply our modeling approach to seven data sets from various domains and demonstrate that our approach consistently outperforms stateof-the-art generative topic models. With the learned representations, we show on an average a gain of 9.6% (0.57 Vs 0.52) in precision at retrieval fraction 0.02 and 7.2% (0.582 Vs 0.543) in F1 for text categorization.

http://arxiv.org/abs/1808.03793v1

cs.IR, cs.CL, cs.LG

Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng, Huang He, Hua Wu

In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers' arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems.

http://arxiv.org/abs/1808.03733v2

cs.CL, cs.IR, cs.LG

STTM: A Tool for Short Text Topic Modeling

Jipeng Qiang, Yun Li, Yunhao Yuan, Wei Liu, Xindong Wu

Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic understanding of textual content. As a rising research field, short text topic modeling presents a new and complementary algorithmic methodology to supplement regular text topic modeling, especially targets to limited word co-occurrence information in short texts. This paper presents the first comprehensive open-source package, called STTM, for use in Java that integrates the state-of-the-art models of short text topic modeling algorithms, benchmark datasets, and abundant functions for model inference and evaluation. The package is designed to facilitate the expansion of new methods in this research field and make evaluations between the new approaches and existing ones accessible. STTM is open-sourced at https://github.com/qiang2100/STTM.

http://arxiv.org/abs/1808.02215v1

cs.IR

Ontology-Grounded Topic Modeling for Climate Science Research

Jennifer Sleeman, Tim Finin, Milton Halem

In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.

http://arxiv.org/abs/1807.10965v2

cs.CL, cs.AI, I.2.4; I.2.6; I.2.7

Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints

Kaveh Bastani, Hamed Namavari, Jeffry Shaffer

A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be used to evaluate the effectiveness of the CFPB regulations and expectations on financial institutions in creating a consumer oriented culture that treats consumers fairly and prioritizes consumer protection in their decision making processes. The proposed approach can be easily operationalized as a decision support system to automate detection of emerging topics in consumer complaints. Hence, the technology-human partnership between the proposed approach and the CFPB team could certainly improve consumer protections from unfair, deceptive or abusive practices in the financial markets by providing more efficient and effective investigations of consumer complaint narratives.

http://arxiv.org/abs/1807.07468v1

cs.IR, cs.LG, stat.ML

Quantifying time-dependent Media Agenda and Public Opinion by topic modeling

Sebastián Pinto, Federico Albanese, Claudio O. Dorso, Pablo Balenzuela

The mass media plays a fundamental role in the formation of public opinion, either by defining the topics of discussion or by making an emphasis on certain issues. Directly or indirectly, people get informed by consuming news from the media. Naturally, two questions appear: What are the dynamics of the agenda and how the people become interested in their different topics? These questions cannot be answered without proper quantitative measures of agenda dynamics and public attention. In this work we study the agenda of newspapers in comparison with public interests by performing topic detection over the news. We define Media Agenda as the distribution of topic's coverage by the newspapers and Public Agenda as the distribution of public interest in the same topic space. We measure agenda diversity as a function of time using the Shannon entropy and differences between agendas using the Jensen-Shannon distance. We found that the Public Agenda is less diverse than the Media Agenda, especially when there is a very attractive topic and the audience naturally focuses only on this one. Using the same methodology we detect coverage bias in newspapers. Finally, it was possible to identify a complex agenda-setting dynamics within a given topic where the least sold newspaper triggered a public debate via a positive feedback mechanism with social networks discussions which install the issue in the Media Agenda.

http://arxiv.org/abs/1807.05184v3

physics.soc-ph

Cooperative opinion dynamics on multiple interdependent topics: Modeling and analysis

Hyo-Sung Ahn, Quoc Van Tran, Minh Hoang Trinh, Kevin L. Moore, Mengbin Ye, Ji Liu

To model the interdependent couplings of multiple topics, we develop a set of rules for opinion updates of a group of agents. The rules are used to design or assign values to the elements of interdependent weighting matrices. The cooperative and anti-cooperative couplings are modeled in both the inverse-proportional and proportional feedbacks. The behaviors of cooperative opinion dynamics are analyzed using a null space property of state-dependent matrix-weighted Laplacian matrices and a Lyapunov candidate. Various consensus properties of state-dependent matrix-weighted Laplacian matrices are predicted according to the intra-agent network topology and inter-dependency topical coupling topologies.

http://arxiv.org/abs/1807.04406v2

cs.SY

Using Topic Models to Mine Everyday Object Usage Routines Through Connected IoT Sensors

Yanxia Zhang, Hayley Hung

With the tremendous progress in sensing and IoT infrastructure, it is foreseeable that IoT systems will soon be available for commercial markets, such as in people's homes. In this paper, we present a deployment study using sensors attached to household objects to capture the resourcefulness of three individuals. The concept of resourcefulness highlights the ability of humans to repurpose objects spontaneously for a different use case than was initially intended. It is a crucial element for human health and wellbeing, which is of great interest for various aspects of HCI and design research. Traditionally, resourcefulness is captured through ethnographic practice. Ethnography can only provide sparse and often short duration observations of human experience, often relying on participants being aware of and remembering behaviours or thoughts they need to report on. Our hypothesis is that resourcefulness can also be captured through continuously monitoring objects being used in everyday life. We developed a system that can record object movement continuously and deployed them in homes of three elderly people for over two weeks. We explored the use of probabilistic topic models to analyze the collected data and identify common patterns.

http://arxiv.org/abs/1807.04343v1

cs.HC

An MCMC Approach to Empirical Bayes Inference and Bayesian Sensitivity Analysis via Empirical Processes

Hani Doss, Yeonhee Park

Consider a Bayesian situation in which we observe $Y \sim p_{\theta}$, where $\theta \in \Theta$, and we have a family $\{ \nu_h, \, h \in \mathcal{H} \}$ of potential prior distributions on $\Theta$. Let $g$ be a real-valued function of $\theta$, and let $I_g(h)$ be the posterior expectation of $g(\theta)$ when the prior is $\nu_h$. We are interested in two problems: (i) selecting a particular value of $h$, and (ii) estimating the family of posterior expectations $\{ I_g(h), \, h \in \mathcal{H} \}$. Let $m_y(h)$ be the marginal likelihood of the hyperparameter $h$: $m_y(h) = \int p_{\theta}(y) \, \nu_h(d\theta)$. The empirical Bayes estimate of $h$ is, by definition, the value of $h$ that maximizes $m_y(h)$. It turns out that it is typically possible to use Markov chain Monte Carlo to form point estimates for $m_y(h)$ and $I_g(h)$ for each individual $h$ in a continuum, and also confidence intervals for $m_y(h)$ and $I_g(h)$ that are valid pointwise. However, we are interested in forming estimates, with confidence statements, of the entire families of integrals $\{ m_y(h), \, h \in \mathcal{H} \}$ and $\{ I_g(h), \, h \in \mathcal{H} \}$: we need estimates of the first family in order to carry out empirical Bayes inference, and we need estimates of the second family in order to do Bayesian sensitivity analysis. We establish strong consistency and functional central limit theorems for estimates of these families by using tools from empirical process theory. We give two applications, one to Latent Dirichlet Allocation, which is used in topic modelling, and the other is to a model for Bayesian variable selection in linear regression.

http://arxiv.org/abs/1807.02191v1

stat.ME, 62F15 (Primary), 62F12 (Secondary)