Tracking Sentiment in Mail: How Genders Differ on Emotional Axes
This work addresses the need for individuals and organizations to understand emotional patterns in email communication, though it is incremental as it applies existing sentiment analysis methods to new data.
The paper tackled the problem of quantifying and tracking emotions in emails by using sentiment analysis and visualizations, resulting in the creation of a large crowdsourced word-emotion lexicon and the identification of marked gender differences in emotion word usage, such as women favoring joy-sadness terms and men preferring fear-trust terms.
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.