Detection of Sentiment Tweets by using NLP Technique and Naive Bayes Classifier Algorithm

Millions of users share opinions on diverse aspects of life and politics every day using micro blogging over the internet. Microbloging websites are rich sources of data for belief mining and sentiment analysis. In this dissertation work, we focus on using Twitter for sentiment analysis for extracting opinions about events, products, people and use it for understanding the current trends or state of the art. Twitter allows its users a limit of only 140 characters; this restriction forces the user to be concise as well as expressive at the same moment. This ultimately makes twitter an ocean of sentiments. Twitter also provides developer friendly streaming. We scuttle datasets over 4 million tweets by a custom designed crawler for sentiment analysis purpose. We propose a hybrid naïve bayes classifier by integrating an English lexical dictionary to the existing machine learning naïve bayes classifier algorithm. Hybrid naïve bayes classifies the tweets in positive and negative classes respectively. Experimental results demonstrate the superiority of hybrid naïve bayes on multi-sized datasets consisting of variety of keywords over existing approaches yielding >90 percent accuracy in general and 98.59 percent accuracy in the best case. In our research, we worked with English; however, the proposed technique can be used with any other language, provided that language lexicon dictionary.