Emotion classification and crowd source sensing; a lexicon based approach

In today's world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users' social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decision-making for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/decision makers in different applications is discussed

A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

In emergency management for mass gatherings, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, the literature shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This paper presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data to validate our framework is described

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

Conversational Agent: Developing a Model for Intelligent Agents with Transient Emotional States

The inclusion of human characteristics (i.e., emotions, personality) within an intelligent agent can often increase the effectiveness of information delivery and retrieval. Chat-bots offer a plethora of benefits within an eclectic range of disciplines (e.g., education, medicine, clinical and mental health). Hence, chatbots offer an effective way to observe, assess, and evaluate human communication patterns. Current research aims to develop a computational model for conversational agents with an emotional component to be applied to the army leadership training program that will allow for the examination of interpersonal skills in future research. Overall, the current research explores the application of the deep learning algorithm to the development of a generalized framework that will be based upon modeling empathetic conversation between an intelligent conversational agent (chatbot) and a human user in order to allow for higher level observation of interpersonal communication skills. Preliminary results demonstrate the promising potential of the seq2seq technique (e.g., through the use of Dialog Flow Chatbot platform) when applied to emotion-oriented conversational tasks. Both the classification and generative conversational modeling tasks demonstrate the promising potential of the current research for representing human to agent dialogue. However, this implementation may be extended by utilizing, a larger more high-quality dataset

Unsupervised and Language Independent Approach to Extremism and Collective Radicalization Understanding

Increasingly in social media, we find cases where groups are organized to protest against something, often in those groups, members with extremist ideologies are inserted. These cases are happing more often, groups are created for the organization of peaceful protests and someone starts a topic with an extremist language leading, sometimes, to a radicalisation of the group. This research aims to create an approach that allows the detection of cases of extremism and collective radicalisation within social networks, this should be done in an unsupervised and independent of language way. The methods used to achieve the intended objectives are the creation of a lexicon of extreme sentiment terms named ExtremeSentiLex and a classifier of extreme sentiment in which the input is the extreme sentiment terms and the social network post. For the development of these tools were used purely statistical natural language processing methods. To validate the ExtremeSentiLex it was applied using the extreme sentiment classifier, the input posts that are analysed are posts from a dataset already validated by the scientific community. For a comparative study, word embeddings are used to expand the first ExtremeSentiLex obtained and a test is also performed in which the ExtremeSentiLex is balanced and applied to a balanced polarity dataset. The results obtained in this content level research that will be available to the scientific community are the ExtremeSentiLex and several datasets that were evaluated by us regarding the presence of extreme sentiment. At the level of tests performed when the ExtremeSentiLex was validated, the level of precision in finding extreme sentiment at the correct polarity was very high. When applying word embeddings the results dropped. Regarding the ExtremeSentiLex and balanced dataset, the results were very positive. It has been concluded that our dataset is suitable for the application in detecting extreme sentiments in text. Furthermore, it was found that with the help of linguistic and psychological experts the ExtremeSentiLex could be improved. However, this investigation aimed to do so using purely statistical methods. This goal has been successfully achieved.Cada vez mais nos social medias encontramos grupos que se organizam para protestarem contra algo e, muitas vezes, nesses mesmos grupos por vezes estão inseridos membros com ideologias extremistas, com o intuito de destabilizar a ordem publica e espalhar os seus ideias recorrendo ao terror. Verifica-se que estes casos são cada vez mais recorrentes, ao criar-se um grupo específico cuja finalidade é a realização de protestos pacíficos com objetivos liberais e concretos, existe muitas vezes alguém que inicia um tópico com linguagem extremista. E, daqui, justificado pela influência de grupo, é possível ter-se em consideração a possibilidade de radicalização coletiva. O objetivo desta investigação é criar uma abordagem para deteção de casos de extremismo e radicalização coletiva em redes sociais e isto deve ser feito de forma não supervisionada e independente da língua. Os métodos utilizados foram: a criação de um léxico de termos de sentimento extremo denominado ExtremeSentiLex e de um classificador de sentimentos extremos em que o input são os termos de sentimento extremo e os posts de redes sociais. Para o desenvolvimento destas ferramentas foram utilizados métodos de processamento da linguagem natural puramente estatísticos. Sendo que, para podermos validar o ExtremeSentiLex este foi aplicado recorrendo ao classificador de sentimentos extremos e aos posts de input que são analisados que são posts de datasets já validados pela comunidade cientifica. Para um estudo comparativo, são utilizados word embeddings para expandir o ExtremeSentiLex obtido e é também feito um teste em que o ExtremeSentiLex é balanceado e aplicado a um dataset também balanceado a nível da polaridade de sentimentos. Os resultados obtidos nesta investigação e que serão disponibilizados para a comunidade cientifica são: o ExtremeSentiLex e datasets, que foram avaliados, relativamente à presença de sentimentos extremos; Os testes efetuados aquando da validação do ExtremeSentiLex: o nível de precisão ao encontrar sentimentos extremos na polaridade correta foi muito elevada. Já aquando da aplicação dos word embeddings os resultados pioraram; Com ExtremeSentiLex e dataset balanceados, os resultados melhoraram. Concluí-se que o ExtremeSentiLex é adequado para a deteção de sentimentos extremos em texto. Detetou-se ainda que com a ajuda de especialistas na área da linguística e da psicologia o ExtremeSentiLex poderia ser aprimorado. Contudo o objetivo desta investigação era apenas fazê-lo recorrendo a métodos puramente estatísticos

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow

Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge. Objective: In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time. Method: We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions. Results: We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style. Conclusion: We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.Comment: Preprint, to appear in Information and Software Technolog