A comparison between two Spanish sentiment lexicons in the twitter sentiment analysis task

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9 Scopus citations

Abstract

Sentiment analysis aims to determine people’s opinions towards certain entities (e.g., products, movies, people, etc.). In this paper we describe experiments performed to determine sentiment polarity on tweets of the Spanish corpus used in the TASS workshop. We explore the use of two Spanish sentiment lexicons to find out the effect of these resources in the Twitter sentiment analysis task. Rule based and supervised classification methods were implemented and several variations over those approaches were performed. The results show that the information of both lexicons improve the accuracy when is provided as a feature to a Naïve Bayes classifier. Despite the simplicity of the proposed strategy, the supervised approach obtained better results than several participant teams of the TASS workshop and even the rule based approach overpass the accuracy of one team which used a supervised algorithm.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - IBERAMIA 2016 - 15th Ibero-American Conference on AI 2016, Proceedings
EditorsHugo Jair Escalante, Manuel Montes-y-Gomez, Alberto Segura, Juan de Dios Murillo
PublisherSpringer Verlag
Pages127-138
Number of pages12
ISBN (Print)9783319479545
DOIs
StatePublished - 2016
Event15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016 - San Jose, Costa Rica
Duration: 23 Nov 201625 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10022 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016
Country/TerritoryCosta Rica
CitySan Jose
Period23/11/1625/11/16

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