Extract Stock Sentiment from Twitter Data

It is a well-known interest to predict stock market movements. Nowadays, social media is perfectly reprehensible for the public's mood and opinion on current events. In general, Twitter[1] has attracted a lot of attention from researchers to research the public's emotions. A fascinating field of research has been the stock market prediction based on public sentiments expressed on twitter. Previous studies concluded that the overall public mood collected from Twitter could well be correlated with the Dow Jones Industrial Average. The stock market prediction attempts to determine a company stock's future value. The company should make a profit if the future stock price can be predicted successfully. The aim of this research is to analyze how well a company's movements in stock prices, rising and falling, are associated with the public views expressed in the company's tweets. Knowing the perspective of the reader from a piece of text is the intention of analyzing sentiment. The present paper used textual representations to examine public feelings in tweets, Word2vec[2] for analysis of public sentiments in tweets. In this paper, they applied sentiment analysis and supervised machine learning concepts to tweets derived from twitter and explored the connection between a company's stock market movements and tweet feelings. Positive news and tweets about a company in social media will definitely encourage people to invest in that company's stocks.