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El 26/11/2021 se han entregado los premios a las mejores tesis doctorales y trabajos fin de máster otorgados por el Colegio Oficial de Ingenieros de Telecomunicación (COIT) de 2020 y 2021. En esta edición han sid ...

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The journal paper DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques, by Oscar Araque, Lorenzo Gatti, Jacopo Staiano and Marco Guerini has been published at the IEEE Transactions on Affective Computing (6.288 Impact Factor, Q1 JCR-2018).

The paper is available at the following URL: https://ieeexplore.ieee.org/document/8798675

A green open access version is available at arXiv.

DOI: 10.1109/TAFFC.2019.2934444

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.