Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM

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

Emotional Design: An Overview

Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing