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Author Ruz, G.A.; Henriquez, P.A.; Mascareno, A.
Title Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers Type
Year 2020 Publication Future Generation Computer Systems-The International Journal Of Escience Abbreviated Journal Futur. Gener. Comp. Syst.
Volume 106 Issue Pages 92-104
Keywords Bayesian network classifiers; Twitter data; Sentiment analysis; Bayes factor; Support vector machines; Random forests
Abstract Sentiment analysis through machine learning using Twitter data has become a popular topic in recent years. Here we address the problem of sentiment analysis during critical events such as natural disasters or social movements. We consider Bayesian network classifiers to perform sentiment analysis on two datasets in Spanish: the 2010 Chilean earthquake and the 2017 Catalan independence referendum. In order to automatically control the number of edges that are supported by the training examples in the Bayesian network classifier, we adopt a Bayes factor approach for this purpose, yielding more realistic networks. The results show the effectiveness of using the Bayes factor measure as well as its competitive predictive results when compared to support vector machines and random forests, given a sufficient number of training examples. Also, the resulting networks allow to identify the relations amongst words, offering interesting qualitative information to historically and socially comprehend the main features of the event dynamics. (C) 2020 Elsevier B.V. All rights reserved.
Address [Ruz, Gonzalo A.; Henriquez, Pablo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: gonzalo.ruz@uai.cl;
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-739x ISBN Medium
Area Expedition Conference
Notes WOS:000527320000009 Approved
Call Number UAI @ eduardo.moreno @ Serial 1145
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