Tracking Sentiment in Mail: How Genders Differ on Emotional Axes

By Saif M. Mohammad, Tony Yang. 2011

178 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets (2013, methodology): “The manually created lexicons we used include the NRC Emotion Lexicon (Mohammad and Turney, 2010; Mohammad and Yang, 2011) (about 14,000 words), the MPQA Lexicon (Wilson et al.”
Gender and Power: How Gender and Gender Environment Affect Manifestations of Power (2014, unknown): “Mohammad and Yang (2011) analyzed the way gender affects the expression of sentiments in text, while we are interested in how gender relates to manifestations of organizational power.”
Using Hashtags to Capture Fine Emotion Categories from Tweets (2015, background): “Liu Mohammad and Yang (2011) worked on email data.”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1815–1827, Seattle, Washington, USA, 18-21 October 2013. c©2013 Association for Computational Linguistics”
Key Female Characters in Film Have More to Talk About Besides Men: Automating the Bechdel Test (2015, background): “There has been much work in the computational sciences community on studying gender differences in the way language is used by men versus women (Peersman et al., 2011; Mohammad and Yang, 2011; Bamman et al., 2012; Schwartz et al., 2013; Bam-man et al., 2014; Prabhakaran et al., 2014).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “Several studies have analyzed the differences in emotions in language used by men and women in these contexts (Mohammad & Yang, 2011b; Grijalva et al., 2014; Montero, Munezero, & Kakkonen, 2014).”
Target-Dependent Twitter Sentiment Classification with Rich Automatic Features (2015, methodology): “, 2005], HL5 [Hu and Liu, 2004], and NRC emotion lexicon6 [Mohammad and Yang, 2011], integrating them to filter the context.”
Sentiment Analysis of Short Informal Texts (2014, unknown): “…novels (Boucouvalas, 2002; John, Boucouvalas, & Xu, 2006; Francisco & Gerv´as, 2006; Mohammad & Yang, 2011), emails (Liu, Lieberman, & Selker, 2003; Mohammad & Yang, 2011), blogs (Neviarouskaya, Prendinger, & Ishizuka, 2011; Genereux & Evans, 2006; Mihalcea & Liu, 2006), and tweets (Mohammad,…”
How Translation Alters Sentiment (2016, methodology): “…(Bellegarda, 2010), novels (Boucouvalas, 2002; John, Boucouvalas, & Xu, 2006; Mohammad & Yang, 2011), emails (Liu, Lieberman, & Selker, 2003; Mohammad & Yang, 2011), blogs (Neviarouskaya, Prendinger, & Ishizuka, 2011; Genereux & Evans, 2006; Mihalcea & Liu, 2006), and tweets (Mohammad,…”