Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 106, Issue -, Pages 92-104Publisher
ELSEVIER
DOI: 10.1016/j.future.2020.01.005
Keywords
Bayesian network classifiers; Twitter data; Sentiment analysis; Bayes factor; Support vector machines; Random forests
Categories
Funding
- CONICYT-Chile [Fondecyt 1180706, Fondecyt 1190265]
- Basal (CONICYT)-CMM, Chile
- Doctoral scholarship, Chile [2015-21150790]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available