Today we have access to unprecedented amounts of literary texts. However, search still relies heavily on key words. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in both individual books and across very large collections. We introduce the concept of emotion word density, and using the Brothers Grimm fairy tales as example, we show how collections of text can be organized for better search. Using the Google Books Corpus we show how to determine an entity's emotion associations from co-occurring words. Finally, we compare emotion words in fairy tales and novels, to show that fairy tales have a much wider range of emotion word densities than novels.
Automatic analysis of emotions in text has so far had to rely on small emotion lexicons. The WordNet Affect Lexicon (WAL) @cite_17 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 We use the NRC Emotion Lexicon @cite_8 @cite_21 , a large set of human-provided word--emotion association ratings, in our experiments. Please send an e-mail to saif.mohammad@nrc-cnrc.gc.ca to obtain the latest version of the NRC Emotion Lexicon.
@cite_8 @cite_21 @cite_3 @cite_17
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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. Even though considerable attention has been given to semantic orientation of words and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how we create a high-quality, moderate-sized emotion lexicon using Mechanical Turk. In addition to questions about emotions evoked by terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We perform an extensive analysis of the annotations to better understand the distribution of emotions evoked by terms of different parts of speech. We identify which emotions tend to be evoked simultaneously by the same term and show that certain emotions indeed go hand in hand. In this paper we present a linguistic resource for the lexical representation of affective knowledge. This resource (named WORDNETAFFECT) was developed starting from WORDNET, through a selection and tagging of a subset of synsets representing the affective