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

Sentiment Analysis of Events in Social Media The growing popularity of Online Social Networks has open new research directions and perspectives for content analysis, i.e., Network Analysis and Natural Language Processing. From the perspective of information spread, the Network Analysis community propose Event Detection. This approach focuses on the network features, without an indepth analysis of the textual content, summarization being a preferred method. Natural Language Processing analyses only the textual content, not integrating the graphbased structure of the network. To address these limitations, we propose a method that bridges the two directions and integrates contentawareness into networkawareness. Our method uses event detection to extract topics of interest and then applies sentiment analysis on each event. The obtained results have high accuracy, proving that our method determines with high precision the overall sentiment of the detected events.