Twitter Data Predicting Stock Price Using Data Mining Techniques
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Authors
Patel, Umang
Issue Date
2016-04-01
Type
Presentation
Language
en_US
Keywords
Data mining , Naive Bayes classification , Stock price , Twitter , Decision tree , Text mining
Alternative Title
Abstract
In this project, we apply sentiment analysis and data mining techniques to discover the correlation between “public sentiment” and “market sentiment”. We use twitter data to predict public mood and use the predicted mood and previous days’ NASDAQ values to predict the stock market movements. We foremost look for a correlation between twitter sentiment and stock prices. Secondly, we determine which words in tweets correlate with changes in stock prices by doing a post analysis of price change and tweets. Also, we discover the relationship between tweets of the vital Twitter user related to the stock and the corresponding one stock price behavior. Lastly, we will try to analyze trending mood on twitter of Top Gainers and Top losers. We achieved this by mining tweets using Twitter’s search API and subsequently processing them for analysis using Sentimental Analysis. For the task of determining sentiment, we test the effectiveness of three data mining techniques: Naive Bayes classification, Decision Tree, and Text Mining.