Predicting Stock Market Movement Based on Twitter Data and News Articles Using Sentiment Analysis and Fuzzy Logic

Abstract

Social media has become an integral part of everyone’s life. Twitter is the most prevalent social networking service where millions of users share information astronomically everyday. Aggregation of these tweets provides a reflection of public sentiment which has a notable impact on the Stock Market. The objective of the proposed method is to predict the impact of Tweets and News Articles on Stock Market and provide insights to the investors to help them decide whether or not to invest in a company. The paper proposes a method to improve the efficiency of the existing methodologies by including news articles and adding specific weights to each tweet based on authenticity, followers of the twitterer and it’s retweet count for scrutinizing the public sentiment. It also considers sentiments expressed via emoticons and converts prolonged words into their normal form to increase the efficiency of sentiment analysis. Further, it uses N-gram representation for Feature Extraction and performs sentiment analysis on the collected tweets and news articles using Natural Language Processing and classifies the result into negative, positive or neutral using Naive Bayes classifier. The empirical results displayed in graphical format show that the proposed system can predict the fluctuations in the stock prices based on the sentiment analysis performed on previous day’s collected tweets and news articles.