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

Oscar Araque*, Lorenzo Gatti, Jacopo Staiano, Marco Guerini

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
125 Downloads (Pure)

Abstract

Several lexica for sentiment analysis have been developed; 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. They are often exploited as a building block for developing emotion recognition learning models, and/or used as baselines to which the performance of the models can be compared. In this work, we contribute two new resources, that we call DepecheMood++ (DM++): 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 performance on datasets and tasks of varying degree of domain-specificity. Also, we report an extensive comparative analysis against other available emotion lexica and state-of-the-art supervised approaches, showing that DepecheMood++ emerges as the best-performing non-domain-specific lexicon in unsupervised settings. We also observe that simple learning models on top of DM++ can provide more challenging baselines. We finally introduce embedding-based methodologies to perform a) vocabulary expansion to address data scarcity and b) vocabulary porting to new languages in case training data is not available.
Original languageEnglish
Pages (from-to)496-507
Number of pages12
JournalIEEE transactions on affective computing
Volume13
Issue number1
Early online date14 Aug 2019
DOIs
Publication statusPublished - 2022

Keywords

  • Emotion analysis
  • Emotion lexicon
  • Machine learning
  • Natural language processing
  • Word embeddings

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