Twitter sentiment analysis using machine learning algorithms

In the era where social network is considered as a means to express people’s opinions and emotions on various topics, it becomes important to understand those views and get in- sights from it. Twitter is one of the most popular networking media for individuals to express their opinions using tweets on any subject of their choice. All of these tweets is ”data” for the marketing company, which they can mine and ex- tract useful information to enhance their products. Data is considered to be the most valuable resource right now and we have various technologies to analyze, manage, process and integrate them. The aim of this paper is to mine emotions or sentiments from the available data (tweets) from social media mainly Twitter. Various sentiments can be seen when a user posts or tweets about a recent incident, or a newly released movie or a brand new product. These sentiments help us under- stand the reception of that particular subject. Sentiment Classification of twitter data is basically categorizing the tweets posted by individuals based on polarity or emotions such as Positive, Negative and Neutral. The tweets by every users vary based on the usage of language, emoticons, hash- tags etc which needs to be first preprocessed and converted into a standard format. After preprocessing, useful features needs to be extracted to perform Sentiment Analysis using various Machine Learning techniques.