Social Media and Stock Market Prediction: A Big Data Approach

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 wo...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2021, Vol.67 (2), p.2569-2583
Main Authors: Javed Awan, Mazhar, Shafry Mohd Rahim, Mohd, Nobanee, Haitham, Munawar, Ashna, Yasin, Awais, Mohd Zain Azlanmz, Azlan
Format: Article
Language:English
Subjects:
Big Data
Customer services
Decision trees
Digital media
Logistics
Machine learning
Regression
Regression analysis
Sales
Securities markets
Social networks
Unstructured data
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Summary: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.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.014253