Paper: Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News

ACL ID P14-2070
Title Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

While many lexica annotated with words polarity are available for sentiment anal- ysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting ? in a totally automated way ? a high- coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way ?crowd-sourced? affective annotation im- plicitly provided by readers of news ar- ticles from rappler.com. By provid- ing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na??ve ap- proach, our experiments show the benefi- cial impact of harvesting social media data for affective lexicon building.