Self-attention for Twitter sentiment analysis in Spanish

Issue title: Special section: Selected papers of LKE 2019

Guest editors: David Pinto, Vivek Singh and Fernando Perez

Article type: Research Article

Authors: González, José Ángel^*; | Hurtado, Lluís-F. | Pla, Ferran

Affiliations: VRAIN: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera sn, 46022, València, Spain

Correspondence: [*] Corresponding author. José Ángel González, VRAIN: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera sn, 46022, València, Spain. E-mail: [email protected].

Abstract: This paper describes our proposal for Sentiment Analysis in Twitter for the Spanish language. The main characteristics of the system are the use of word embedding specifically trained from tweets in Spanish and the use of self-attention mechanisms that allow to consider sequences without using convolutional nor recurrent layers. These self-attention mechanisms are based on the encoders of the Transformer model. The results obtained on the Task 1 of the TASS 2019 workshop, for all the Spanish variants proposed, support the correctness and adequacy of our proposal.

Keywords: Twitter, sentiment analysis, transformer encoders

DOI: 10.3233/JIFS-179881

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2165-2175, 2020