Issue: 2024/Vol.34/No.2, Pages 85-107
PREDICTING STOCK MARKET BY SENTIMENT ANALYSIS AND DEEP LEARNING
Süreyya Özöğür Akyüz
, Pınar Karadayı Ataş , Aymane Benkhaldoun
Cite as: S. Ö. Akyüz, P. K. Ataş, A. Benkhaldoun. Predicting stock market by sentiment analysis and deep learning. Operations Research and Decisions 2024: 34(2), 85-107. DOI 10.37190/ord240206
Abstract
The stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea of textual data mining has recently come up in debates about stock market forecasts. In this study, five of the largest firms’ historical stock prices were used to train two deep learning models—long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), then the results of all the models were compared. To connect price value fluctuations with the general public, sentiment scores were offered in addition to stock price values by employing natural language processing techniques (TextBlob) to tweets.
Keywords: stock market, Twitter, deep learning, sentiment analysis
Received: 26 October 2022 Accepted: 28 April 2024
Published online: 8 July 2024