DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News
This provides a resource for researchers and practitioners in natural language processing to perform emotion analysis more effectively, though it is incremental as it builds on existing lexicon methods.
The authors tackled the lack of high-coverage lexica for emotion analysis by creating DepecheMood, a lexicon of roughly 37,000 terms with emotion scores, which achieved new state-of-the-art performances in unsupervised regression and classification tasks.
While many lexica annotated with words polarity are available for sentiment analysis, 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 implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a naïve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.