Social media is used to analyze political campaigns, stock market, movies, medicines, agriculture etc. Twitter a microblogging website where users read and write millions of tweets on a variety of topics daily. This project attempts to analyze the sentiment of the people for the election candidates based upon the live opinions and emotions. The focus of our project is to assign the polarity to each tweet that is whether the user expresses a positive or negative opinion. With the tweets that are extracted, we try to find how frequent their emotions change. We are also trying to classify and differentiate the sentiments of the people before and after the election based on the tweets they upload. The location of the twitter user is used to classify the geographical area which in turn helps to analyze the emotions of people of different areas. Our project uses the Naïve-Bayes approach in R language and R Studio for processing the textual data.
Twitter, R, Sentiment Analysis, Naïve Bayes Algorithm
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