Difference between revisions of "Stock Prediction using Social Media Analysis"
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| − | By [http://reu.mscs.mu.edu/index.php/User:Scoyne Scott Coyne | + | By [http://reu.mscs.mu.edu/index.php/User:Scoyne Scott Coyne] |
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| − | + | Stock market prices are becoming more and more volatile, largely due to improvements in technology and increased trading volume. Speculation affects business owners, investors, and policymakers alike. While these seemingly unpredictable trends continue, investors and consumers take to social media to share thoughts and opinions. We use information shared over StockTwits, a social media platform for investors, to better understand and predict individual stock prices. We designed and implemented three machine learning models to forecast stock prices using the dataset collected from StockTwits. We also evaluated our models with conclusions drawn from previous researchers in this field. Our first model found no correlation between general StockTwits postings and stock price. However, our second and third models considered a novel approach and successfully filtered through the twits to find important posts. These important twits could predict stock price movements with greater accuracy (average around 65%) based on sentiment analysis and smart user identification. We consider a user “smart” based on number of likes, follower count and more importantly how often the user is right about a stock. | |
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Latest revision as of 18:13, 3 August 2017
By Scott Coyne Mentor Dr. Praveen Madiraju
Goals and Milestones
1) Complete literature survey of similar projects
2) Compile all social media and stock price info into single data-frame
3) determine sentiment of posts and classify them by value
4) create multiple machine learning models to predict stock prices and evaluate each of them
5) calculate weighted scores for users based on their influence and apply that to the model
6) create a high level architecture diagram of the system
7) produce a final project report
Abstract
Stock market prices are becoming more and more volatile, largely due to improvements in technology and increased trading volume. Speculation affects business owners, investors, and policymakers alike. While these seemingly unpredictable trends continue, investors and consumers take to social media to share thoughts and opinions. We use information shared over StockTwits, a social media platform for investors, to better understand and predict individual stock prices. We designed and implemented three machine learning models to forecast stock prices using the dataset collected from StockTwits. We also evaluated our models with conclusions drawn from previous researchers in this field. Our first model found no correlation between general StockTwits postings and stock price. However, our second and third models considered a novel approach and successfully filtered through the twits to find important posts. These important twits could predict stock price movements with greater accuracy (average around 65%) based on sentiment analysis and smart user identification. We consider a user “smart” based on number of likes, follower count and more importantly how often the user is right about a stock.
