DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques

TL;DR

This paper introduces two new emotion lexicons, one for English and one for Italian, and demonstrates how simple techniques can enhance emotion recognition performance across various datasets and settings.

Contribution

The work extends an existing English emotion lexicon and creates a new Italian version, showcasing simple methods to improve emotion analysis models.

Findings

Enhanced emotion lexicons for English and Italian.

Simple techniques improve model performance in supervised and unsupervised settings.

Effective across diverse datasets and domain-specific tasks.

Abstract

Several lexica for sentiment analysis have been developed and made available in the NLP community. While most of these come with word polarity annotations (e.g. positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g. happiness, sadness) have recently attracted significant attention. Such lexica are often exploited as a building block in the process of developing learning models for which emotion recognition is needed, and/or used as baselines to which compare the performance of the models. In this work, we contribute two new resources to the community: a) an extension of an existing and widely used emotion lexicon for English; and b) a novel version of the lexicon targeting Italian. Furthermore, we show how simple techniques can be used, both in supervised and unsupervised experimental settings, to boost performances on datasets and tasks of varying degree of domain-specificity.