Cyber Times International Journal of Technology & Management

With the explosive growth of user generated messages, Twitter has become a social site where millions of users can exchange their opinion. Sentiment analysis on Twitter data has provided an economical and effective way to expose public opinion timely, which is critical for decision making in various domains. For instance, a company can study the public sentiment in tweets to obtain users' feedback towards its products; while a politician can adjust his/her position with respect to the sentiment change of the public. There have been a large number of research studies and industrial applications in the area of public sentiment tracking and modeling. Millions of users share their opinions on Twitter, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. Therefore it has attracted attention in both academia and industry. This paper surveys three machine learning approaches for Automatic Sentiment Analysis of Twitter Messages and proposes a hybrid approach combining lexicon-based approach and classification using Naïve Bayes classifier.