pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Sarcasm Using Different Forms Of Incongruity
Authors: Aditya Joshi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sarcasm Detection, Sentiment Analysis, Topic Models, Word Embeddings

Sarcasm is a form of verbal irony that is intended to express contempt or ridicule. Often quoted as a challenge to sentiment analysis, sarcasm involves use of words of positive or no polarity to convey negative sentiment. Incongruity has been observed to be at the heart of sarcasm understanding in humans. Our work in sarcasm detection identifies different forms of incongruity and employs different machine learning techniques to capture them. This talk will describe the approach, datasets and challenges in sarcasm detection using different forms of incongruity. We identify two forms of incongruity: incongruity which can be understood based on the target text and common background knowledge, and incongruity which can be understood based on the target text and additional, specific context. The former involves use of sentiment-based features, word embeddings, and topic models. The latter involves creation of author{'}s historical context based on their historical data, and creation of conversational context for sarcasm detection of dialogue.
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
Authors: Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pad{\'o}, Roman Klinger
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, General Classification, Sentiment Analysis, Stance Detection

There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification). For the more challenging task of detecting discrete emotions following the definitions of Ekman and Plutchik, however, there are much fewer data sets, and notably no resources for the social media domain. This paper contributes to closing this gap by extending the \textit{SemEval 2016 stance and sentiment dataset}with emotion annotation. We (a) analyse annotation reliability and annotation merging; (b) investigate the relation between emotion annotation and the other annotation layers (stance, sentiment); (c) report modelling results as a baseline for future work.
Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups
Authors: Matthias Hartung, Roman Klinger, Franziska Schmidtke, Lars Vogel
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extremist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right-wing extremism, we frame the task as ranking each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust performance (up to 0.81 F$_1$ score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Authors: Maximilian K{\"o}per, Evgeny Kim, Roman Klinger
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, Sentence, Sentiment Analysis

Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms. We combine three main information sources in a random forrest regressor, namely (1), manually created resources, (2) automatically extended lexicons, and (3) the output of a neural network (CNN-LSTM) for sentence regression. All three feature sets perform similarly well in isolation ({\mbox{$\approx$}} .67 macro average Pearson correlation). The combination achieves .72 on the official test set (ranked 2nd out of 22 participants). Our analysis reveals that performance is increased by providing cross-emotional intensity predictions. The automatic extension of lexicon features benefit from domain specific embeddings. Complementary ratings for affective norms increase the impact of lexicon features. Our resources (ratings for 1.6 million twitter specific words) and our implementation is publicly available at \url{http://www.ims.uni-stuttgart.de/data/ims_emoint}.
Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
Authors: Pranav Goel, Devang Kulshreshtha, Prayas Jain, Kaushal Kumar Shukla
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis

The paper describes the best performing system for EmoInt - a shared task to predict the intensity of emotions in tweets. Intensity is a real valued score, between 0 and 1. The emotions are classified as - anger, fear, joy and sadness. We apply three different deep neural network based models, which approach the problem from essentially different directions. Our final performance quantified by an average pearson correlation score of 74.7 and an average spearman correlation score of 73.5 is obtained using an ensemble of the three models. We outperform the baseline model of the shared task by 9.9{\%} and 9.4{\%} pearson and spearman correlation scores respectively.
Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental Questions
Authors: Iryna Gurevych
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Common Sense Reasoning

Mining arguments from natural language texts, parsing argumentative structures, and assessing argument quality are among the recent challeng-es tackled in computational argumentation. While advanced deep learning models provide state-of-the-art performance in many of these tasks, much attention is also paid to the underly-ing fundamental questions. How are arguments expressed in natural language across genres and domains? What is the essence of an argument{'}s claim? Can we reliably annotate convincingness of an argument? How can we approach logic and common-sense reasoning in argumentation? This talk highlights some recent advances in computa-tional argumentation and shows why researchers must be both {``}surfers{''} and {``}scuba divers{''}.
Toward Stance Classification Based on Claim Microstructures
Authors: Filip Boltu{\v{z}}i{\'c}, Jan {\v{S}}najder
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Argument Mining, Classification, Fine-Grained Opinion Analysis, General Classification, Opinion Mining, Stance Classification

Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining. We propose a framework for representing claims as microstructures, which express the beliefs, judgments, and policies about the relations between domain-specific concepts. In a proof-of-concept study, we manually build microstructures for over 800 claims extracted from an online debate. We test the so-obtained microstructures on the task of claim stance classification, achieving considerable improvements over text-based baselines.
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media
Authors: Viktor Pekar, Jane Binner
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: regression, Sentiment Analysis, Time Series, Time Series Analysis

Consumer spending is an important macroeconomic indicator that is used by policy-makers to judge the health of an economy. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11{\%} to 18{\%} for all the three models, in comparison to models that used only autoregressive predictors.
Understanding human values and their emotional effect
Authors: Alex Balahur, ra
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

Emotions can be triggered by various factors. According to the Appraisal Theories (De Rivera, 1977; Frijda, 1986; Ortony et al., 1988; Johnson-Laird and Oatley, 1989) emotions are elicited and differentiated on the basis of the cognitive evaluation of the personal significance of a situation, object or event based on {``}appraisal criteria{''} (intrinsic characteristics of objects and events, significance of events to individual needs and goals, individual{'}s ability to cope with the consequences of the event, compatibility of event with social or personal standards, norms and values). These differences in values can trigger reactions such as anger, disgust (contempt), sadness, etc., because these behaviors are evaluated by the public as being incompatible with their social/personal standards, norms or values. Such arguments are frequently present both in mainstream media, as well as social media, building a society-wide view, attitude and emotional reaction towards refugees/immigrants. In this demo, I will talk about experiments to annotate and detect factual arguments that are linked to human needs/motivations from text and in consequence trigger emotion in the media audience and propose a new task for next year{'}s WASSA.
Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions
Authors: Rebeca Padilla L{\'o}pez, Fabienne Cap
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis

In this work, we present a first attempt to investigate multi-emoji expressions and whether they behave similarly to multiword expressions in terms of non-compositionality. We focus on the combination of the frog and the hot beverage emoji, but also show some preliminary results for other non-compositional emoji combinations. We use off-the-shelf sentiment analysers as well as manual classifications to approach the compositionality of these emoji combinations.
Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus
Authors: Giulia Donato, Patrizia Paggio
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: POS, Sentiment Analysis

In this paper we present an annotated corpus created with the aim of analyzing the informative behaviour of emoji {--} an issue of importance for sentiment analysis and natural language processing. The corpus consists of 2475 tweets all containing at least one emoji, which has been annotated using one of the three possible classes: Redundant, Non Redundant, and Non Redundant + POS. We explain how the corpus was collected, describe the annotation procedure and the interface developed for the task. We provide an analysis of the corpus, considering also possible predictive features, discuss the problematic aspects of the annotation, and suggest future improvements.
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
Authors: Kishaloy Halder, Lahari Poddar, Min-Yen Kan
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual{'}s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.
Towards an integrated pipeline for aspect-based sentiment analysis in various domains
Authors: Orph{\'e}e De Clercq, Els Lefever, Gilles Jacobs, Tijl Carpels, V{\'e}ronique Hoste
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentiment Analysis, Term Extraction

This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, banking and human resources. The two latter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA subtasks, aspect term extraction, aspect category classification and aspect polarity classification.
Building a SentiWordNet for Odia
Authors: Gaurav Mohanty, Abishek Kannan, Radhika Mamidi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Sentiment Analysis, Translation, Word Alignment

As a discipline of Natural Language Processing, Sentiment Analysis is used to extract and analyze subjective information present in natural language data. The task of Sentiment Analysis has acquired wide commercial uses including social media monitoring tasks, survey responses, review systems, etc. Languages like English have several resources which aid in the task of Sentiment Analysis. SentiWordNet and Subjectivity WordList are examples of such tools and resources. With more data being available in native vernacular, language-specific SentiWordNet(s) have become essential. For resource poor languages, creating such SentiWordNet(s) is a difficult task to achieve. One solution is to use available resources in English and translate the final source lexicon to target lexicon via machine translation. Machine translation systems for the English-Odia language pair have not yet been developed. In this paper, we discuss a method to create a SentiWordNet for Odia, which is resource-poor, by only using resources which are currently available for Indian languages. The lexicon created, would serve as a tool for Sentiment Analysis related task specific to Odia data.
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
Authors: Egor Lakomkin, Ch Bothe, rakant, Stefan Wermter
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Text Classification

The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to build a system that assigns emotion intensity values. Emotion intensity estimation is a challenging problem given the short length of the tweets, the noisy structure of the text and the lack of annotated data. To solve this problem, we developed an ensemble of two neural models, processing input on the character. and word-level with a lexicon-driven system. The correlation scores across all four emotions are averaged to determine the bottom-line competition metric, and our system ranks place forth in full intensity range and third in 0.5-1 range of intensity among 23 systems at the time of writing (June 2017).
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
Authors: Vladimir Andryushechkin, Ian Wood, James O{'} Neill
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, regression, Speech Recognition, Word Embeddings

This paper describes the entry NUIG in the WASSA 2017 (8th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis) shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BLSTM features). Experiments were carried out on several other candidate features, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BLSTM model were selected through a non-exhaustive ad-hoc search.
Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets
Authors: Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Term Extraction

Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80{\%}. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.
PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets
Authors: Henrique Santos, Renata Vieira
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: regression, Sentiment Analysis

Linguistic Inquiry and Word Count (LIWC) is a rich dictionary that map words into several psychological categories such as Affective, Social, Cognitive, Perceptual and Biological processes. In this work, we have used LIWC psycholinguistic categories to train regression models and predict emotion intensity in tweets for the EmoInt-2017 task. Results show that LIWC features may boost emotion intensity prediction on the basis of a low dimension set.
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets
Authors: Hardik Meisheri, Rupsa Saha, Priyanka Sinha, Lipika Dey
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis, Word Embeddings

This paper describes our approach to the Emotion Intensity shared task. A parallel architecture of Convolutional Neural Network (CNN) and Long short term memory networks (LSTM) alongwith two sets of features are extracted which aid the network in judging emotion intensity. Experiments on different models and various features sets are described and analysis on results has also been presented.
YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
Authors: You Zhang, Hang Yuan, Jin Wang, Xue-jie Zhang
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis

In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results.
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
Authors: Md. Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, Pushpak Bhattacharyya
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, feature selection, Sentence, Sentence Embeddings, Word Embeddings

This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017). We propose a Long short term memory (LSTM) based architecture cascaded with Support Vector Regressor (SVR) for intensity prediction. We also employ Particle Swarm Optimization (PSO) based feature selection algorithm for obtaining an optimized feature set for training and evaluation. System evaluation shows interesting results on the four emotion datasets i.e. anger, fear, joy and sadness. In comparison to the other participating teams our system was ranked 5th in the competition.
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
Authors: Sreekanth Madisetty, Maunendra Sankar Desarkar
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, Sentiment Analysis, Word Embeddings

In this paper, we describe a method to predict emotion intensity in tweets. Our approach is an ensemble of three regression methods. The first method uses content-based features (hashtags, emoticons, elongated words, etc.). The second method considers word n-grams and character n-grams for training. The final method uses lexicons, word embeddings, word n-grams, character n-grams for training the model. An ensemble of these three methods gives better performance than individual methods. We applied our method on WASSA emotion dataset. Achieved results are as follows: average Pearson correlation is 0.706, average Spearman correlation is 0.696, average Pearson correlation for gold scores in range 0.5 to 1 is 0.539, and average Spearman correlation for gold scores in range 0.5 to 1 is 0.514.
Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach
Authors: Antonio Moreno-Ortiz
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis

In this paper we describe Tecnolengua Group{'}s participation in the shared task on emotion intensity at WASSA 2017. We used the Lingmotif tool and a new, complementary tool, Lingmotif Learn, which we developed for this occasion. We based our intensity predictions for the four test datasets entirely on Lingmotif{'}s TSS (text sentiment score) feature. We also developed mechanisms for dealing with the idiosyncrasies of Twitter text. Results were comparatively poor, but the experience meant a good opportunity for us to identify issues in our score calculation for short texts, a genre for which the Lingmotif tool was not originally designed.
YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
Authors: Yuanye He, Liang-Chih Yu, K. Robert Lai, Weiyi Liu
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: General Classification, Sentiment Analysis, Word Embeddings

The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.