Social Media and Stock Market Prediction: A Big Data Approach

TLDR

Big data comprises large, diverse datasets from digital sources, including social media sentiment, and its rapid growth presents volume and variety challenges across sectors such as retail and finance. The paper implements multiple machine learning models using Spark MLlib in PySpark to predict stock price movements. The authors applied Spark MLlib’s linear regression, generalized linear regression, random forest, decision tree, naive Bayes, and logistic regression to historical price data from ten leading companies. Experimental results show that linear regression, random forest, and generalized linear regression achieve 80–98% accuracy, while decision tree predictions performed poorly.

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.