TWITTER SENTIMENT ANALYSIS WITH ENHANCED REDUCTION OPTIMIZATION

Abstract

Sentiment analysis, also refers as opinion mining, is a sub machine learning task where we want to determine which the general sentiment of a given document is. Using machine learning techniques and natural language processing we can extract the subjective information of a document and try to classify it according to its polarity such as positive, neutral or negative. It is a really useful analysis since this could possibly determine the overall opinion about selling objects, or predict stock markets for a given company like, if most people think positive about it, possibly its stock markets will increase, and so on. Sentiment analysis is actually far from to be solved since the language is very complex (objectivity/subjectivity, negation, vocabulary, grammar,...) but it is also why it is very interesting to working on.In this work classification is performed on tweets from Twitter into positive or negative sentiment by building a model based on probabilities. Twitter is a micro blogging website where people can share their feelings quickly and spontaneously by sending a tweets limited by 140 characters. You can directly address a tweet to someone by adding the target sign @ or participate to a topic by adding an hashtag # to your tweet. Because of the usage of Twitter, it is a perfect source of data to determine the current overall opinion about anything.