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Author (up) Ruz, G.A.; Henriquez, P.A.; Mascareno, A. doi  openurl
  Title Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers Type Journal Article
  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 no  
  Call Number UAI @ eduardo.moreno @ Serial 1145  
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