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    Stock Price Prediction Using Sentiment Analysis and LSTM

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    Final Thesis Submission
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    Author
    Carter, Caymen
    Keyword
    Long Short-Term Memory neural networks
    sentiment analysis
    stock price prediction
    LSTM model
    Kaggle
    Readers/Advisors
    Reale, Michael J., Chair
    Confer, Amos, Reader
    Urban, Christopher, Reader
    Date Published
    2022-05
    
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    Show full item record
    URI
    http://hdl.handle.net/20.500.12648/7334
    Abstract
    This work presents multiple Long Short-Term Memory neural networks used in con- junction with sentiment analysis to predict stock prices over time. Multiple datasets and input features are used on a LSTM model to decipher which features produce the best output predictions and if there is correlation to the sentiment of posts and the rising of a stock. This project uses embedding based sentiment analysis on a dataset collected from Kaggle which includes over one million posts made on the subreddit r/wallstreetbets. This subreddit recently came under fire by the media with the shorting of Gamestop in the stock market. It was theorized that this subreddit was working as a collective to drive up the price of multiple stock, therefore hurting large corporations such as hedge funds that had large short positions on multiple stocks.
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