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

Multimodal Emotion Classification

Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98\% is achieved with a training data of 550k posts.Comment: Accepted at the 2nd Emoji Workshop co-located with The Web Conference 201

ReWIND: A CBT-Based Serious Game to Improve Cognitive Emotion Regulation and Anxiety Disorder

Games have shown successful intervention outcomes and can be used to complement the treatment of anxiety disorders. However, current serious game solutions are designed to be task-based rather than story-based. We present ReWIND, a serious role-playing game (RPG) applying cognitive behavioral therapy (CBT) to design anxiety-relevant storylines and game mechanics. ReWIND advances state-of-the-art mental health games by seamlessly integrating CBT elements and strategies into the game’s storytelling so players can learn how CBT is applied in anxiety scenarios as they play through the game. Our goal is to examine the effectiveness of ReWIND in improving cognitive emotion regulation and anxiety disorders. Through a randomized controlled trial, 40 participants were recruited, of whom half were randomly assigned to play ReWIND while the others worked on a non-game task. Anxiety and cognitive emotion regulation levels were measured before and after the interventions. Our findings show ReWIND significantly reduces the severity level of anxiety symptoms and trait anxiety levels and increases perceived control of anxiety better than the non-game task. ReWIND also obtained positive ratings for its usability and practicality in real life, helping participants to cope better with anxiety disorders

Semi-Automatic Content Analysis of Qualitative Data

Qualitative content analysis is commonly used by social scientists to understand the practices of the groups they study, but it is often infeasible to manually code a large text corpus within a reasonable time frame and budget. To address this problem, we are building a software tool to assist social scientists performing content analysis. We present our semi-automatic system that leverages natural language processing (NLP) and machine learning (ML) techniques for initial automatic coding, which human coders then review and correct. Through active learning, these human-verified annotations are subsequently used to train a higher performing model for machine annotation. We discuss design strategies adopted to optimize the system performance.publishedye

Finding the Link between Cyberbullying and Suicidal Behaviour among Adolescents in Peninsular Malaysia

Social media engagement has contributed to the rise of cyberbullying, which has recently triggered tragic suicides among adolescents. The objective of this cross-sectional study is to determine the prevalence of cyberbullying, suicidal behaviour, and their association among adolescents in Peninsular Malaysia. The study was conducted among 1290 secondary school adolescents aged between 13 and 17 years old in Peninsular Malaysia using a self-administered and anonymous online questionnaire. Our findings reveal that the prevalence of cyberbullying victimization and perpetrator is 13.7% and 3.8%, respectively. The prevalence of suicidal behaviour among adolescents is 17.1%, in which 11.9% had suicidal thoughts, 10.2% had a suicide plan, and 8.4% had made a suicide attempt. Multiple logistic regression shows that adolescents experiencing cyberbullying victimization is a significant risk factor (p < 0.001) for suicidal behaviour after adjusting for other confounders. An alarming number of adolescents in Peninsular Malaysia found to be involved in cyberbullying and its significant association with suicidal behaviour warrant the need to strengthen current interventions. Since the study was conducted during the COVID-19 pandemic, our findings make an important contribution in reporting current trends in cyberbullying and suicide among adolescents, especially when they are becoming ever-more present in cyberspaces