Social Media and Stock Market Prediction: A Big Data Approach

Awan, Mazhar Javed, Rahim, Mohd Shafry Mohd, Nobanee, Haitham, Munawar, Ashna, Yasin, Awais and Zain, Azlan Mohd (2021) Social Media and Stock Market Prediction: A Big Data Approach. CMC-COMPUTERS MATERIALS & CONTINUA, 67 (2). pp. 2569-2583.

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Official URL: http://dx.doi.org/10.32604/cmc.2021.014253

Abstract

Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public social media are sharing a vast quantity of sentiments related to sales price and products. Challenges of big data include volume and variety in both structured and unstructured data. In this paper, we implemented several machine learning models through Spark MLlib using PySpark, which is scalable, fast, easily integrated with other tools, and has better performance than the traditional models. We studied the stocks of 10 top companies, whose data include historical stock prices, with MLlib models such as linear regression, generalized linear regression, random forest, and decision tree. We implemented naive Bayes and logistic regression classification models. Experimental results suggest that linear regression, random forest, and generalized linear regression provide an accuracy of 80%–98%. The experimental results of the decision tree did not well predict share price movements in the stock market.

Item Type:	Article
Additional Information:	Source info: M. J. Awan, M. Shafry, H. Nobanee, A. Munawar, A. Yasin et al., "Social media and stock market prediction: a big data approach," Computers, Materials & Continua, vol. 67, no.2, pp. 2569–2583, 2021
Uncontrolled Keywords:	Big data, analytics, artificial intelligence, machine learning, stock market, social media, business analytics
Divisions:	Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures
Depositing User:	Symplectic Admin
Date Deposited:	04 Aug 2021 10:20
Last Modified:	18 Jan 2023 21:34
DOI:	10.32604/cmc.2021.014253
Open Access URL:	https://www.techscience.com/cmc/v67n2/41320
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3132360