Stock Price Prediction Using Natural Language Processing and Machine Learning
Date
2023-08-09
Authors
Amer, Ahmed
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Abstract
Predicting the stock market is an infamous problem that many people have tried to solve. Can real time textual data in the form of tweets be used to predict stock movements? In this project, the use of different natural language processing methods are used to process twitter data to try to find out their sentiment. Furthermore, based on the sentiment, further analysis is done using machine learning techniques to try and predict next day returns for individual stocks. Two and Three different features were used to try and predict the next day's percentage change. The metrics used to assess the methodology were accuracy, precision and cumulative percentage gain or loss using a specific strategy or method. The results of this project suggest that using tweets as input for natural language processing and machine learning can achieve average accuracies and result in strategies that have consistently beaten the market in terms of cumulative returns.
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Natural Language Processing, Machine Learning, NLP, ML, KNN, RF, K-Nearest Neigbours, Random Forests