DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News

TL;DR

This paper introduces DepecheMood, a high-coverage emotion lexicon derived automatically from crowd-annotated news, demonstrating improved performance in emotion analysis tasks by leveraging social media data.

Contribution

The paper presents a novel automated method to create a large emotion lexicon from crowd-sourced news reader annotations, enhancing emotion analysis capabilities.

Findings

Achieved state-of-the-art results in emotion classification and regression tasks.

Demonstrated the effectiveness of social media data for affective lexicon construction.

Produced a lexicon with approximately 37,000 emotion-annotated terms.

Abstract

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 na\"{\i}ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.