Reviewerly/exHarmony huggingface dataset:
Siambert: Siamese Bert-based Code Search

Code Search is a practical tool that helps developers navigate growing source code repositories by connecting natural language queries with code snippets. Platforms such as StackOverflow resolve coding questions and answers; however, they cannot perform a semantic search through the code. Moreover, poorly documented code adds more complexity to search for code snippets in repositories. To tackle this challenge, this paper presents Siambert, a BERT-based model that gets the question in natural language and returns relevant code snippets. The Siambert architecture consists of two stages, where the first stage, inspired by Siamese Neural Network, returns the top K relevant code snippets to the input questions, and the second stage ranks the given snippets by the first stage. The experiments show that Siambert outperforms non-BERT-based models having improvements that range from 12% to 39% on the Recall@1 metric and improves the inference time performance, making it 15x faster than standard BERT models.
Out-of-distribution in Human Activity Recognition

With the growing interest of the research community in making deep learning (DL) robust and reliable, detecting out-of-distribution (OOD) data has become critical. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model. This capability increases the model’s reliability since this model provides a class prediction solely at incoming data similar to the training one. OOD detection is well established in computer vision problems. However, it remains relatively under-explored in other domains such as time series (i.e., Human Activity Recognition (HAR)). Since uncertainty has been a critical driver for OOD in vision-based models, the same component has proven effective in time-series applications.We plan to address the OOD detection problem in HAR with time-series data in this work. To test the capability of the proposed method, we define different types of OOD for HAR that arise from realistic scenarios. We apply an ensemble-based temporal learning framework that incorporates uncertainty and detects OOD for the defined HAR workloads. In particular, we extract OODs from popular benchmark HAR datasets and use the framework to separate those OODs from the in-distribution (ID) data. Across all the datasets, the ensemble framework outperformed the traditional deep-learning method (our baseline) on the OOD detection task.
Fundamentals of Opinion Mining

Mining techniques in computer science have been evolving for the last two decades. Opinion mining is the latest buzzword in this evolution and goes to a deeper level to understand the drive behind people's behavior. Due to the richness of social media opinions, emotions, and sentiments, opinion mining examines that how people feel about a given situation, be it positive or negative. This chapter primarily focuses on explaining the fundamentals of opinion mining along with sentiment analysis. It covers the brief evolution in mining techniques in the last decade. The chapter elaborates on the significance of opinion mining in today's scenario and its features. It also includes a section to discuss the applications, challenges and research scope in opinion mining.
Approaches to Sentiment Analysis on Product Reviews

Purchase decisions are better when opinions/reviews about products are considered. Similarly, reviewing customer feedback help in improving the sale and ultimately benefit the business. Web 2.0 provides various platforms such as Twitter, Facebook, etc. where one can comment, review, or post to express his/her happiness, anger, disbelief, sadness toward products, people, etc. To computationally analyze the sentiments in text requires a better understanding of the technologies used in sentiment analysis. This chapter gives a comprehensive understanding about the techniques used in sentiment analysis. Machine learning approaches are mostly used for sentiment analysis. Whereas, as per the text and required results, lexicon-based approaches are also used for the same purpose. This chapter includes the discussion on the evaluation parameters for the sentiment analysis. This chapter would also highlight ontology approach for sentiment analysis and outstanding contributions made in this field. Keywords: Sentiment Analysis, Product reviews, Supervised learning, Unsupervised learning, Social networking websites, Ontology
Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.
Deep Learning for Sentiment Analysis

Sentiment analysis is an important area of natural language processing that can help inform business decisions by extracting sentiment information from documents. The purpose of this chapter is to introduce the reader to selected concepts and methods of deep learning and show how deep models can be used to increase performance in sentiment analysis. It discusses the latest advances in the field and covers topics including traditional sentiment analysis approaches, the fundamentals of sentence modelling, popular neural network architectures, autoencoders, attention modelling, transformers, data augmentation methods, the benefits of transfer learning, the potential of adversarial networks, and perspectives on explainable AI. The authors' intent is that through this chapter, the reader can gain an understanding of recent developments in this area as well as current trends and potentials for future research.
Applications of Ontology-Based Opinion Mining

Ontology provides a technique to formulate and present queries to databases either stand-alone or web-based. Ontology has been conceived to produce reusable queries to extract rules matching them, and hence, it saves time and effort in creating new ontology-based queries. Ontology can be incorporated in the machine learning process, which hierarchically defines the relationship between concepts, axioms, and terms in the domain. Ontology rule mining has been found to be efficient as compared to other well-known rule mining methods like taxonomy and decision trees. In this chapter, the authors carry out a detailed survey about ontology-related information comprising classification, creation, learning, reuse, and application. The authors also discuss the reusability and the tools used for reusing ontology. Ontology has a life cycle of its own similar to the software development life cycle. The classification-supervised machine learning technique and clustering and the unsupervised machine learning are supported by the ontology. The authors also discuss some of the open issues in creation and application of ontology.
Towards a Sentiment Analysis Model Based on Semantic Relation Analysis

Sentiment analysis is an important new field of research that has attracted the attention not only of researchers, but also businesses and organizations. In this article, the authors propose an effective model for aspect-based sentiment analysis for Vietnamese. First, sentiment dictionaries and syntactic dependency rules were combined to extract reliable word pairs (sentiment - aspect). They then relied on ontology to group these aspects and determine the sentiment polarity of each. They introduce two novel approaches in this work: 1) in order to “smooth” the sentiment scaling (rather than using discrete categories of 1, 0, and -1) for fined-grained classification, then extract multi-word sentiment phrases instead of sentiment words, and 2) the focus is not only on adjectives but also nouns and verbs. Initial evaluations of the system using real reviews show promising results.
Sentiment Analysis in Crisis Situations for Better Connected Government

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis

Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.
Deep Learning Approaches for Textual Sentiment Analysis

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.
Integrating Semantic Acquaintance for Sentiment Analysis

The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.
Real Time Sentiment Analysis

Data is the most valuable resource. We have a lot of unstructured data generated by the social media giants Twitter, Facebook, and Google. Unfortunately, analytics on unstructured data cannot be performed. As the availability of the internet became easier, people started using social media platforms as the primary medium for sharing their opinions. Every day, millions of opinions from different parts of the world are posted on Twitter. The primary goal of Twitter is to let people share their opinion with a big audience. So, if the authors can effectively analyse the tweets, valuable information can be gained. Storing these opinions in a structured manner and then using that to analyse people's reactions and perceptions about buying a product or a service is a very vital step for any corporate firm. Sentiment analysis aims to analyse and discover the sentiments behind opinions of various people on different subjects like commercial products, politics, and daily societal issues. This research has developed a model to determine the polarity of a keyword in real time.
Classification Approach for Sentiment Analysis Using Machine Learning

A utilization of the computational semantics is known as natural language processing or NLP. Any opinion through attitude, feelings, and thoughts can be identified as sentiment. The overview of people against specific events, brand, things, or association can be recognized through sentiment analysis. Positive, negative, and neutral are each of the premises that can be grouped into three separate categories. Twitter, the most commonly used microblogging tool, is used to gather information for research. Tweepy is used to access Twitter's source of information. Python language is used to execute the classification algorithm on the information collected. Two measures are applied in sentiment analysis, namely feature extraction and classification. Using n-gram modeling methodology, the feature is extracted. Through a supervised machine learning algorithm, the sentiment is graded as positive, negative, and neutral. Support vector machine (SVM) and k-nearest neighbor (KNN) classification models are used and demonstrated both comparisons.
A Deep Neural Network Model for Cross-Domain Sentiment Analysis

Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.
Sentiment Analysis Techniques, Tools, Applications, and Challenge

Sentiment (opinion) refers to the feelings of a human being, which are generally reflected through speech and writing in a particular natural language. The analysis of these sentiments are therefore carried with the help of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing research, political reviews, policy making, decision making, customer service, etc. In this chapter the authors include the introduction to sentiment analysis, various approaches for classification of sentiment analysis, various tools used, the application areas, challenges, and future research direction in this most demanding area.
A Novel Aspect Based Framework for Tourism Sector with Improvised Aspect and Opinion Mining Algorithm

With the growth of e-commerce web sites, the demand of writing reviews on these portals have gained huge popularity. This huge data must be mined to analyze the opinion and for making better decisions in different domains. In this paper, we have proposed an aspect based opinion mining algorithm for the tourism domain. It first determines the aspects, and then extracts the opinion words related to the aspects. The opinion words are provided a score based on the Senti-Wordnet and the final score of each aspect is calculated by the summation of the scores of the opinions. The final score is visualized depicting ranking of scores of different aspects for different hotels.
Public Security Sentiment Analysis on Social Web

This article presents (1) the results of a literature review on social web mining and sentiment analysis on public security; (2) the idea of a framework for the analytical process involved in the literature review themes; and (3) a research agenda with a perspective for future studies, considering some elements of the analytical process. The literature review was based on searches of five databases: Scopus, IEEE Xplore, Web of Science, ScienceDirect, and Springer Link. Search strings were applied to retrieve literature material of four kinds, without defining an initial time milestone, to get the historical register of publications associated with the main thematic. After some filtering, primary and secondary findings were separated, enabling the identification of elements for the framework. Finally, the research agenda is presented, containing a set of three research artifacts related to the proposed framework.
Analyzing Social Emotions in Social Network Using Graph Based Co-Ranking Algorithm

Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about “events,” and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. Online content exhibits rich temporal dynamics, and diverse real-time user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored. This article describes tracking and analyzing public sentiment on social networks and finding the possible reasons causing these variations. It is important to find the decision from public views and opinion in different domain. They can be used to discover special topics or aspects in one text collection in comparison with another background text collection. The implemented method attains the 95% accuracy while predict the sentiments from the social websites and the 96.3% of the opinion rate with minimum time.
A Comparative Study of Different Classification Techniques for Sentiment Analysis

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.
Photonic-aware Neural Networks for Packet Classification in URLLC scenarios

Ultra Reliable Low Latency Communications (URLLC) scenarios require very low latency and high reliability, imposing an optimization of every aspect of 5G data processing, transmission, and networking. Artificial Intelligence (AI)-based tools can be helpful resources in this context, enhancing multiple functionalities, from network resource allocation to network security. In this paper we propose a solution placed at the next generation eNB (gNB)-Central Unit (CU) level, relying on Neural Networks (NNs), capable of classifying incoming packets. The developed system increases the security of 5G and B5G architectures, protecting the 5G Core (5GC) from potential attacks. To comply with URLLC requirements on latency, we present an architecture leveraging photonic hardware to speed-up NN computations. The proposed solution, namely Photonic-Aware Neural Network (PANN), complies with physical layer constraints raised by photonic analog computing and can achieve high throughput and time-of-flight latency. The classification performance of the devised PANN model has been assessed through simulation on the distilled Kitsune dataset, suited for 5G scenarios. The experiments proved that PANN significantly lowers the chance of transmitting malicious packets to the 5GC with a classification performance increasing with the bit resolution supported by the analog photonic physical layer.
Identity and an Extended Situation Theory

We focus on identity as an equivalence relation (supporting Leibniz's Law and the Principle of Substitution) and how identity hypotheses are addressed and supported in law enforcement. The dominate notion of identify in computer science is of something like a profile; identifying in our terms can have such a profile as one side of an identity as long as it is interpreted as denoting. Our examples are from law enforcement, but we intend our framework to apply generally to identifying people. We use a modified version of Barwise and Perry's situation theory to capture how evidence accrues to identity hypotheses. We extend the relational theory of meaning of situation semantics to encompass an extended version of the causal theory of reference that regards traces of a person (e.g., fingerprints) and their copies as devices denoting that person. These objects stitch together the situations relevant to an investigation. We extend situation theory with higher-order elements motivated by our analysis of criminal investigations, event statements, and thematic relations taken from sentential semantics. We present a hypothetical criminal scenario (which we have presented in focus groups) and show how we encode it into RDF in terms of our ontologies.