DepecheMood++: A Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
| Autor: | Lorenzo Gatti, Oscar Araque, Marco Guerini, Jacopo Staiano |
|---|---|
| Přispěvatelé: | Human Media Interaction |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Emotion lexicon Vocabulary Computer science Emotion analysis media_common.quotation_subject 02 engineering and technology Lexicon computer.software_genre Computer Science - Computers and Society Simple (abstract algebra) Computers and Society (cs.CY) Machine learning 0202 electrical engineering electronic engineering information engineering media_common Block (data storage) Computer Science - Computation and Language business.industry Natural language processing 05 social sciences Sentiment analysis Human-Computer Interaction Sadness Word embeddings Happiness 020201 artificial intelligence & image processing Artificial intelligence 0509 other social sciences 050904 information & library sciences business Computation and Language (cs.CL) computer Software Word (computer architecture) |
| Zdroj: | IEEE transactions on affective computing, 13(1), 496-507. IEEE |
| ISSN: | 2371-9850 1949-3045 |
| DOI: | 10.1109/taffc.2019.2934444 |
| Popis: | 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. 12 pages, 2 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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