User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis

Abstract

The stock market prices of the company vary in a daily fashion. The social media pattern usage of the company can be determined to find the sentiment score values. The 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. The 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. The 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. This 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. The 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 naïve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index.

Public Concern and Awareness of National Parks in China: Evidence from Social Media Big Data and Questionnaire Data

“…The mining and visualization of large amount of data generated by users in social media has provided new ideas and methods for domestic and foreign scholars to conduct scientific research [36] (Stieglitz et al, 2018). Twitter, a widely used social media platform, has become an important tool for science communication in the research community [37] (Joanne et al, 2022), and many scholars have conducted research using data collected from Twitter [38][39][40][41][42][43][44] Rumbidzai (2021) analyzed the text content and emotion of tweet data from users referencing seven South African National Parks, with the goal of understanding public concern and emotion regarding national parks and provide a reference for managers of national parks [45]. Pragya (2021) assessed the content and sentiment of 9,000 tweets posted by more than 4,500 Twitter users in more than 90 countries to gain insight into the public's concern for and awareness of national parks in Nepal [46].…”

Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency

“…On the other hand, Lachanski and Pav (2017) suggest that Twitter sentiment cannot predict stock market movements to a satisfactory degree. Sentiment analysis seems to be a common tool used (Achyutha et al., 2022; Herrera et al., 2022; Kraaijeveld & De Smedt, 2020; Maqsood et al., 2022; Zimbra et al., 2018), but fails to provide a strong predictive relationship (Reboredo & Ugolini, 2018). Hence, other researchers suggest that further scrutiny is required, since sentiment analysis alone might not have enough descriptive power to capture the information exchange in online platforms (Banerjee, 2022; Evangelopoulos et al., 2012; Kalamara et al., 2022).…”

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

“…The SC updates the position using equation ( 8) if 1   , else the position is updated using equation (13). By doing this, optimal position of sc ( ) *  (that is, optimal reduced keywords) can be obtained.…”