Comparing sentiment analysis tools on gitHub project discussions

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe context of this work is situated in the rapidly evolving sphere of Natural Language Processing (NLP) within the scope of software engineering, focusing on sentiment analysis in software repositories. Sentiment analysis, a subfield of NLP, provides a potent method to parse, understand, and categorize these sentiments expressed in text. By applying sentiment analysis to software repositories, we can decode developers’ opinions and sentiments, providing key insights into team dynamics, project health, and potential areas of conflict or collaboration. However, the application of sentiment analysis in software engineering comes with its unique set of challenges. Technical jargon, code-specific ambiguities, and the brevity of software-related communications demand tailored NLP tools for effective analysis. The study unfolds in two primary phases. In the initial phase, we embarked on a meticulous investigation into the impacts of expanding the training sets of two prominent sentiment analysis tools, namely, SentiCR and SentiSW. The objective was to delineate the correlation between the size of the training set and the resulting tool performance, thereby revealing any potential enhancements in performance. The subsequent phase of the research encapsulates a practical application of the enhanced tools. We employed these tools to categorize discussions drawn from issue tickets within a varied array of Open-Source projects. These projects span an extensive range, from relatively small repositories to large, well-established repositories, thus providing a rich and diverse sampling ground.O contexto deste trabalho situa-se na esfera em rápida evolução do Processamento de Linguagem Natural (PLN) no âmbito da engenharia de software, com foco na análise de sentimentos em repositórios de software. A análise de sentimentos, um subcampo do PLN, fornece um método poderoso para analisar, compreender e categorizar os sentimentos expressos em texto. Ao aplicar a análise de sentimentos aos repositórios de software, podemos decifrar as opiniões e sentimentos dos desenvolvedores, fornecendo informações importantes sobre a dinâmica da equipe, a saúde do projeto e áreas potenciais de conflito ou colaboração. No entanto, a aplicação da análise de sentimentos na engenharia de software apresenta desafios únicos. Jargão técnico, ambiguidades específicas do código e a breviedade das comunicações relacionadas ao software exigem ferramentas de PLN personalizadas para uma análise eficaz. O estudo se desenvolve em duas fases principais. Na fase inicial, embarcamos em uma investigação meticulosa sobre os impactos da expansão dos conjuntos de treinamento de duas ferramentas proeminentes de análise de sentimentos, nomeadamente, SentiCR e SentiSW. O objetivo foi delinear a correlação entre o tamanho do conjunto de treinamento e o desempenho da ferramenta resultante, revelando assim possíveis aprimoramentos no desempenho. A fase subsequente da pesquisa engloba uma aplicação prática das ferramentas aprimoradas. Utilizamos essas ferramentas para categorizar discussões retiradas de bilhetes de problemas em uma variedade diversificada de projetos de código aberto. Esses projetos abrangem uma ampla gama, desde repositórios relativamente pequenos até repositórios grandes e bem estabelecidos, fornecendo assim um campo de amostragem rico e diversificado

Violence Detection in Social Media-Review

Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content.Keywords: Machine learning, natural language processing, violence, social media, convolution neural networ

Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data

Credible Review Detection with Limited Information using Consistency Analysis

Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.Comment: PhD thesis, Mar 201

Sentiment Analysis on YouTube Comments : Analysis of prevailing attitude towards Nokia Mobile Phones

The volume of textual data, more specifically, the magnitude of opinionated text on social media, has increased the interest of companies to closely analyze what their customers have to say about them and their products. This thesis explores the possibility of performing aspect-based sentiment analysis with YouTube comments. The comments on Nokia Mobile phones are the subject of the study in this thesis. First, manual labeling was performed to identify the aspect terms and sentiment and then categorize the aspects based on the aspect’s functionality on the phone. From the categorization, it was found out that people mainly have shown negative sentiment towards multiple aspects of the phone with maximum negative attitude towards the price of the phone. On the other hand, the only aspect that could gather a positive attitude was the phone’s-built quality. The result shows that there are multiple phone aspects that HMD Global can consider for current and future product improvement. Further, this study used the labeled data to perform supervised learning to classify the aspects and the aspect sentiment from the comments. With two features extraction techniques, BoW and TF-IDF, this paper has explored the performance of different machine learning models on YouTube comments. The models show good results for aspect classification; however, the model’s performance could be further improved for aspect sentiment classification. Overall, little attention to this area has been discussed because of the complexity, highly unstructured, and noisy nature of text on YouTube. However, despite the challenges, this platform can be valuable in producing insightful analysis, as presented in this thesis