A Review towards the Sentiment Analysis Techniques for the Analysis of Twitter Data

Any opinion of an individual through which the feelings, attitudes and thoughts can be expressed is known as sentiment. The kinds of data analysis which is attained from the news reports, user reviews, social media updates or microblogging sites is called sentiment analysis which is also known as opinion mining. The reviews of individuals towards certain events, brands, product or company can be known through sentiment analysis. The responses of general public are collected and improvised by researchers to perform evaluations. The popularity of sentiment analysis is growing today since the numbers of views being shared by people on the microblogging sites are also increasing. All the sentiments can be categorized into three different categories called positive, negative and neutral. Twitter, being the most popular microblogging site, is used to collect the data to perform analysis. Tweepy is used to extract the source data from Twitter. Python language is used in this research to implement the classification algorithm on the collected data. The features are extracted using N-gram modeling technique. The sentiments are categorized among positive, negative and neutral using a supervised machine learning algorithm known as K-Nearest Neighbor.