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An Investigation of Social Media Influence on Stock Market Price Prediction Using Sentiment Analysis and Machine Learning

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Dr. Binita Verma, Dr. Swati Namdev
» doi: 10.48047/ecb/2023.12.Si6.029


In Machine Learning (ML) stock market price prediction is one of the complex problem due to volatile nature and dependency on various real world consequences. However, there are a number of efforts and claims are exist which are offering the accurate price prediction. But most of them are not effectively fit for all the scenarios of stock market price prediction. In this paper, we have proposed a ML based stock market price prediction model for investigating the influence of social media information for accurate stock market prediction models. In this context, first we have carried out a survey on recently contributed data models for stock market price prediction. Next, a simple deep neural network is trained on Yahoo Query Language (YQL) database to predict the stock market price. Third, the deep learning model has been extended to incorporate the social media sentiment data with the historical price data for finding influence on price prediction. The experiments on two popular Indian banks namely State Bank of India (SBI) and ICICI bank has been carried out, additionally for inclusion of social media information we have used the Twitter based bank news. The experiments have been carried out and the comparison among their prediction accuracy performed. Additionally, the improvements are reported in different scenarios of experiments. The finding demonstrate the deep learning model provides the accurate prediction of stock market price, additionally the social media information can help to understand the possible price movements and also can improve the accuracy up to 3-5%. Finally some more facts have been identified based on which we have proposed the future extension of the proposed model.

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