A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis

Twitter popularity has increasingly grown in the last few years making influence on the social, political and business aspects of life. Therefore, sentiment analysis research has put special focus on Twitter. Tweet data have many peculiarities relevant to the use of informal language, slogans, and special characters. Furthermore, training machine learning classifiers from tweets data often faces the data sparsity problem primarily due to the large variety of Tweets expressed in only 140-character. In this work, we evaluate the performance of various classifiers commonly used in sentiment analysis to show their effectiveness in sentiment mining of Twitter data under different experimental setups. For the purpose of the study the Stanford Testing Sentiment dataset STS is used. Results of our analysis show that multinomial Naïve Bayes outperforms other classifiers in Twitter sentiment analysis and is less affected by data sparsity.