Research Article User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis Prasad N. Achyutha , 1 Sushovan Chaudhury , 2 Subhas Chandra Bose , 3 Rajnish Kler , 4 Jyoti Surve , 5 and Karthikeyan Kaliyaperumal 6 1 Department of Computer Science and Engineering, East West Institute of Technology, Bangalore, India 2 Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India 3 apar Institute of Engineering and Technology, Patiala, India 4 Motilal Nehru College (Evening), University of Delhi, Delhi, India 5 Department of Information Technology, International Institute of Information Technology, Hinjewadi, Pune, India 6 IT@IoT-HH Campus, Ambo University, Ambo, Ethiopia Correspondence should be addressed to Sushovan Chaudhury; sushovan.chaudhury@gmail.com and Karthikeyan Kaliyaperumal; karthikeyan@ambou.edu.et Received 7 February 2022; Revised 1 March 2022; Accepted 8 March 2022; Published 22 April 2022 Academic Editor: Vijay Kumar Copyright © 2022 Prasad N. Achyutha et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e stock market prices of the company vary in a daily fashion. e social media pattern usage of the company can be determined to find the sentiment score values. e dependency factor between the social media tweet platform and the performance of an organization can have how much effect on the stock prices is determined. e historical data from the Yahoo Finance APIs are taken for the unique company ID and then the probability of stock being good or bad is determined. Also, the tweets related to the company are scanned and analyzed to find the positive and negative scores. e concentration value connected to growth, the intensity of capital expenditure, and the volume of promotion were among the factors utilized in the stock’s modeling. is paper also takes the yearly finances of the end-user based on LIC payments, medical insurance payments, and average rent and then performs a classification of the user. Based on the user classification, companies are recommended to the end-user based on descending order of stock value. e average volume, average price, average market index, average daily turnover, and sentiment discrepancy index are based on the tweets of a company and the predicted value of its performance. For the classification of the user, we make use of the support vector machine algorithm. For the sentiment analysis of the tweets, the na¨ ıve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index. 1. Prime Investigation e communication patterns of the company can be ana- lyzed to understand the performance of the company. e effect of stock prices will depend upon multiple factors like communication on social media, Twitter, and history related to the prices, along with other factors of the stock exchange. e publicly available data related to various companies and stock data obtained from Yahoo Finance can be used to analyze the patterns of stock. e tweets can be converted into a set of statements. Each statement is then analyzed to get the sentimental flow of the overall Twitter data and then generated into a matrix based on Twitter analysis. Find the unique IDs of the products and then the total sentiment of the products is determined. is data can help change the recommendations to end users related to which company stocks are more suitable to trade or purchase. ere are interesting flows among users who make use of unique social media applications [1]. Most of the research in the field of stock recommendation systems is concentrated in two areas: Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 4644855, 9 pages https://doi.org/10.1155/2022/4644855