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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This paper describes an investigation of authorship gender attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a support vector machine learning algorithm. Experiments using a corpus of e-mail documents generated by a large number of authors of both genders gave promising results for author gender categorisation. In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2 . 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