Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

Gonzalo A. Ruz, Pablo A. Henríquez, Aldo Mascareño

Research output: Contribution to journalArticlepeer-review

212 Scopus citations


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.

Original languageEnglish
Pages (from-to)92-104
Number of pages13
JournalFuture Generation Computer Systems
StatePublished - May 2020


  • Bayes factor
  • Bayesian network classifiers
  • Random forests
  • Sentiment analysis
  • Support vector machines
  • Twitter data


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