Title: Evaluating technological impacts on stock market behaviour: a machine learning and NLP approach to socio-economic analysis

Authors: Richa Handa; S. Pavani; Bisahu Ram Sahu; Bijay Kumar Paikaray; Madhusmita Mohanty; Lata Algamkar

Addresses: Department of Computer Science, D. P. Vipra College, Bilaspur (C.G.), 495001, India ' Department of Computer Science, CMD PG College, Bilaspur (C.G.), 495001, India ' Department of Information Technology and Computer Science, Dr. C.V.Raman University, Kota, Bilaspur, Chhattisgarh, 495001, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University, Odisha, 751030, India ' VIT-AP School of Business (VSB), VIT-AP University, Amaravati, Andhra Pradesh, 522241, India ' Artificial Intelligence and Machine Learning, Department of Computer Science and Engineering, Malla Reddy (Deemed to be) University, Maismmaguda, Hyderabad, Telangana, 500043, India

Abstract: Indian stock market is influenced by Politics, finance, and various other internal and foreign issues. It is very challenging for academicians to predict the behaviour of the stock market accurately. Nowadays, people communicate their ideas on social media on various topics depending on what they wish to write. Social media plays a very important role in knowing about the current trends of stock data as people nowadays share their views on social media, whether positive or negative. In this study, we analyse sentiments of people on the stock market using Twitter data and classify it using machine learning techniques to develop an analytical model such as Bernoulli Naïve Bayes, support vector machine (SVM), and Logistic Regression and perform a comparative study to find out which model is outperforming for sentiment analysis of Indian stock market.

Keywords: stock data; semantic analysis; Twitter; social media; Bernoulli Naive Bayes; SVM; support vector machine; socio-economic.

DOI: 10.1504/IJTPM.2025.150713

International Journal of Technology, Policy and Management, 2025 Vol.25 No.4, pp.396 - 411

Received: 30 Oct 2024
Accepted: 17 Mar 2025
Published online: 22 Dec 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article