Abstract:- Sentiment analysis deals with identifying and classifying opinions orsentiments expressed in source text. Social media is generating a vast amountof sentiment rich data in the form of tweets, status updates, blog posts etc.Sentiment analysis of this user generated data is very useful in knowing theopinion of the crowd. Twitter sentiment analysis is difficult compared togeneral sentiment analysis due to the presence of slang words and misspellings.Knowledge base approach and Machine learning approach are the two strategiesused for analysing sentiments from the text. Public and private opinion about awide variety of subjects are expressed and spread continually via numeroussocial media. Twitter is one of the social media that is gaining popularity.Twitter offers organizations a fast and effective way to analyse customers'perspectives toward the critical to success in the market place. Developing aprogram for sentiment analysis is an approach to be used to computationallymeasure customers' perceptions. This project uses knowledge base includingvarious patterns for tweets along with multiple strategies to detect thesentiment expressed in a tweet and if a tweet is genuine or not. Variousmachine learning and knowledge base approaches are used to compare patterns andapply strategies and NLP for sentiment analysis.
Keywords: - NLP(Natural Language Processing), Sentiment Analysis, Machine Learning, PatternMatching, Twitter Data, POS (Part of Speech)