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Article type: Research Article
Authors: Martín-del-Campo-Rodríguez, C.; * | Batyrshin, Ildar | Sidorov, Grigori
Affiliations: Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación (CIC), Av. Juan de Dios Batiz, s/n, 07320, Mexico City, Mexico
Correspondence: [*] Corresponding author. C. Martín-del-Campo-Rodríguez. E-mail: [email protected].
Abstract: Word embeddings have been successfully used in diverse tasks of Natural Language Processing, including sentiment analysis and emotion classification, even though these embeddings do not contain any emotional or sentimental information. This article proposes a method to refine pre-trained embeddings with emotional and sentimental content. To this end, a Multi-output Neural Network is proposed to learn emotions and sentiments simultaneously. The resulting embeddings are tested in emotion classification and sentiment analysis tasks, showing an improvement compared with the pre-trained vectors and other proposes in the state-of-the-art for fine-grained emotion classification.
Keywords: Word embedding, multi-output neural network, VAD, polarity, emotion classification
DOI: 10.3233/JIFS-219354
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
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