Sentiment Analysis of Twitter Data Through Machine Learning Techniques


Cloud computing is a revolutionary technology for businesses, governments, and citizens. Some examples of Software-as-a-Services (SaaS) of cloud computing are banking apps, e-mail, blog, online news, and social networks. In this chapter, we analyze data sets generated by trending topics on Twitter that emerged from Mexican citizens that interacted during the earthquake of September 19, 2017, using sentiment analysis and supervised learning, based on the Ekman’s six emotional model. We built three classifiers to determine the emotions of tweets that belong to the same topic. The classifiers with the best accuracy for predicting emotions were Naive Bayes and support vector machine. We found that the most frequent predicted emotions were happiness, anger, and sadness; also, that 6.5% of predicted tweets were irrelevant. We provide some recommendations about the use of machine learning techniques in sentiment analysis. Our contribution is the expansion of the emotions range, from three (negative, neutral, positive) to six in order to provide more elements to understand how users interact with social media platforms. Future research will include validation of the method with different data sets and emotions, and the addition of new artificial intelligence techniques to improve accuracy.