Stock Market Prediction Using Social Media Sentiments

Predicting the stock price movement is a challenging problem. This paper proposes a novel machine-learning (ML)-based model (denoted TextBlob Hybrid Arima-Garch (TBHAG)) for integrating sentiment analysis in stock market prediction, which uses Twitter data as the source of sentiment information. The proposed model is validated by applying it to predicting the stock movement of the National Stock Exchange of India (denoted NIFTY 50). Our proposed approach consists of capturing the sentiment of investors and traders and studying the effect of this sentiment on the stock market movement. In this approach, the Textblob model is used for analyzing the sentiment from the tweets, and afterward, a hybrid ARIMA-GARCH model is designed and applied for prediction purposes. Simulations are conducted, showing that our proposed TBHAG model can achieve significant improvements over the considered baseline models in terms of prediction accuracy, while also capturing the impact of major events, news, investors, and traders' opinions that can influence the stock market movements.