Tracking Sentiment in Mail: How Genders Differ on Emotional Axes

TL;DR

This paper demonstrates how sentiment analysis and visualization can quantify and compare emotional expressions across different email types and genders, revealing distinct emotional usage patterns.

Contribution

It introduces a large crowdsourced word--emotion lexicon and applies it to analyze gender differences in email sentiment and emotions.

Findings

Women use more joy-sadness words in emails.

Men prefer fear-trust related terms.

Visual tools help track emotional trends in emails.

Abstract

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.