Forecasting Stock Price Movements Based on Opinion Mining and Sentiment Analysis: An Application of Support Vector Machine and Twitter Data

Babak Sohrabi (), Ahmad Khalili Jafarabad () and Ardalan Hadizadeh ()
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Babak Sohrabi: Faculty of Management, University of Tehran
Ahmad Khalili Jafarabad: Faculty of Management, University of Tehran
Ardalan Hadizadeh: Faculty of Management, University of Tehran

Journal of Money and Economy, 2020, vol. 15, issue 3, 235-251

Abstract: Today, social networks are fast and dynamic communication intermediaries that are a vital business tool. This study aims at examining the views of those involved with Facebook stocks so that we can summarize their views to predict the general behavior of this stock and collectively consider possible Facebook stock price movements, and create a more accurate pattern compared to previous patterns. In this study, we have analyzed two statistical samples, the first being a large dataset containing a variety of tweets with an emotional tag. That is, it needed a set that had already been extracted from each individual tweet by a trusted human or machine. Consequently, we have collected posts on Facebook in an eighty-day period. In this study, we used a tagged dataset using Python's programming language and vector-to-word algorithm. The research results show that, we need stock change information, machine learning and sentiment analysis, and on paper we conclude that positive news about a company excites people to have positive opinions about it which in turn results in people encouraging each other to buy and hold stocks. Meanwhile, the opposite trend is also true, but everything will not always be easy and clear, and it is in areas of high complexity and mental uncertainty that the art of using the three elements mentioned above is evident.

Keywords: Social Networking; Stock Prediction; Group Emotion; Collective Emotion; Sentiment Analysis; Opinion Mining; Neural Network (search for similar items in EconPapers)
JEL-codes: C53 M15 (search for similar items in EconPapers)
Date: 2020
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