  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.
