Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity. In the past, sentiment analysis lexicons have been created by experts, such as linguists and behavioural scientists, with strict rules. Lexicon evaluation was also performed by experts or gold standards. In our paper, we propose a crowdsourcing method for lexicon acquisition, which is scalable, cost-effective, and doesn't require experts or gold standards. We also compare crowd and expert evaluations of the lexicon, to assess the overall lexicon quality, and the evaluation capabilities of the crowd.
According to @cite_26 , an emotion is defined with reference to a list. @cite_12 proposed the six basic emotions joy, anger, fear, sadness, disgust, and surprise. Years later, Plutchik @cite_4 proposed the addition of trust and anticipation as basic emotions, and presented a circumplex model of emotions as seen in Figure , which defines emotional contradictions and some of the possible combinations.
@cite_26 @cite_4 @cite_12
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This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and we propose and evaluate several knowledge-based and corpusbased methods for the automatic identification of these emotions in text. 1. Introduction The study of emotion Types of evidence for theories of emotion Some goals for a cognitive theory of emotion 2. Structure of the theory The organisation of emotion types Basic emotions Some implications of the emotions-as-valenced-reactions claim 3. The cognitive psychology of appraisal The appraisal structure Central intensity variables 4. The intensity of emotions Global variables Local variables Variable-values, variable-weights, and emotion thresholds 5. Reactions to events: I. The well-being emotions Loss emotions and fine-grained analyses The fortunes-of-others emotions Self-pity and related states 6. Reactions to events: II. The prospect-based emotions Shock and pleasant surprise Some interrelationships between prospect-based emotions Suspense, resignation, hopelessness, and other related states 7. Reactions to agents The attribution emotions Gratitude, anger, and some other compound emotions 8. Reactions to objects The attraction emotions Fine-grained analyses and emotion sequences 9. The boundaries of the theory Emotion words and cross-cultural issues Emotion experiences and unconscious emotions Coping and the function of emotions Computational tractability. We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder agreement per category and per strength of ratings per subcategory. The respondents achieved an average 70.7 agreement in the most commonly identified emotion categories per text. The categories of high positive affect and pleasantness were most common in our data. Within those categories, the affective terms “enthusiastic”, “active”, “excited”, “pleased”, and “satisfied” had the most consistent ratings of strength of presence in the texts. The textual clues the respondents chose had comparable length and similar key words. Watson and Tellegen’s model appears to be usable as a guide for development of an NLP algorithm for automated identification of emotion in English texts, and the non-expert informants (with college degree and higher) provided sufficient information for future creation of a gold standard of clues per category. This paper describes an experimental study on the detection of emotion from speech. As computer-based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters becomes more important. This study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned to a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.