With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word--emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy--sadness axis, whereas men prefer terms from the fear--trust axis. Finally, we show visualizations that can help people track emotions in their emails.
Automatically analyzing affect in emails has primarily been done for automatic gender identification @cite_10 @cite_0 , but it has relied on mostly on surface features such as exclamations and very small emotion lexicons. The WordNet Affect Lexicon (WAL) @cite_20 has a few hundred words annotated with associations to a number of affect categories including the six Ekman emotions (joy, sadness, anger, fear, disgust, and surprise). WAL: http: wndomains.fbk.eu wnaffect.html General Inquirer (GI) @cite_3 has 11,788 words labeled with 182 categories of word tags, including positive and negative polarity. GI: http: www.wjh.harvard.edu @math inquirer Affective Norms for English Words (ANEW) has pleasure (happy--unhappy), arousal (excited--calm), and dominance (controlled--in control) ratings for 1034 words. ANEW: http: csea.phhp.ufl.edu media anewmessage.html Mohammad and Turney compiled emotion annotations for about 4000 words with eight emotions (six of Ekman, trust, and anticipation).
@cite_0 @cite_3 @cite_10 @cite_20
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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. Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory. While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na " ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building. Automatic recognition of a speaker’s emotions is a natural objective for research, but is difficult to gauge the level of performance that is currently attainable. We describe a study that offers a rough benchmark. Speech data came from five passages of about 100 syllables each. They had been selected following pilot studies because they were effective at evoking specific emotion fear, anger, happiness, sadness, and neutrality. 40 subjects were recorded reading them. A battery of 32 potentially relevant features was extracted using our ASSESS system. They were broadly speaking prosodic, derived from contours tracing the movement of intensity and pitch. They were input to statistical decision mechanisms, of two types. Discriminant analysis uses linear combinations of variables to separate samples that belong to different categories. There are reasons to suspect that linear combination will not be appropriate, so neural net classifiers were also considered. An automatic relevance determination procedure was used to identify the most relevant parameters. Discriminant analysis outperformed the neural networks. Using 90 of the data for training, and testing on the remaining 10 , a classification rate of 55 (+ 0.08 ) was achieved. The most useful predictors covered a variety of properties – intensity (relative to the start of the passage) and its spread; pitch spread; durations of silences, rises in intensity, and syllables; and a property related to the shape of ‘tunes’, the number of inflections in the F0 contour per tune. Many more variables were less important, but nevertheless contributed.