Twitter Sentiment Analysis using Machine Learning

Abstract

As the number of social media users is being higher, many people are sharing various opinions andeach country’s real-time situation by online. Also, the influence of online information is increasingto such an extent that the individual’s actual behavior or situation can be estimated. In this situation,researches to analyze through social media are being actively carried out in order to identify problemsin real life. In this research, we proved that we can infer actual behavior or situation based onindividual social media activities. This research focused on the Twitter platform that is actively usedto express individual emotions in social media platforms. We analyzed tweets of Donald Trump andHillary Clinton who were the 45th presidential candidates of the United States of America. Severalmethodologies like sentiment analysis, topic modeling, and machine learning were used to provecorrelation between Donald Trump’s tweets and his behavior. Through experiment, it proved notonly we can adjust classification and clustering algorithms but also Decision Tree was the most accuratealgorithm. Finally, we proposed the possibility of applying the above method to a system fordetecting anomaly symptoms by concentrating on negative messages. It is expected to provide socialmedia users with sufficient awareness of online activities.