Sentimental Analysis to Predict Stock Market Using in Neutrosophic Time Series

Saravanaraj .S .S¹, Vediyappan Govindan^1,*, Said Broumi², Haewon Byeon^3,*

¹Department of Mathematics, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India

²Laboratory of Information Processing, Faculty of Science Ben M'Sik, University, Hassan II, B.P 7955, Morocco

³Department of AI Big data, Inje University, Gimhae, 50834, Republic of Korea

Emails: saravanarajcibi2000@gmail.com; govindoviya@gmail.com; broumisaid78@gmail.com; byeon@inje.ac.kr

Abstract

This study delves into the innovative use of sentiment analysis in conjunction with neutrosophic time series to forecast stock market trends in various contexts. By meticulously analyzing financial news and social media data, sentiment scores are derived and subsequently integrated into a neutrosophic time series model. This model is uniquely adept at managing uncertainty and indeterminacy, providing a robust framework for prediction. The findings indicate that this integrated approach significantly enhances predictive accuracy and reliability over traditional time series models. This research presents a novel methodology for tackling the intrinsic unpredictability of stock markets, offering a more reliable tool for investors and analysts across diverse financial environments. Additionally, by incorporating sentiment scores from a wide range of sources, the model captures a comprehensive view of market sentiment, reflecting the collective mood and opinions of investors. This comprehensive approach ensures that the predictions are not only accurate but also reflective of real-time market dynamics. Finally, this work highlights the possibility of merging sentiment analysis with sophisticated modeling approaches to change stock market prediction, as well as providing a promising avenue for future financial forecasting research.

Keywords:  Stock Market Prediction; Neutrosophic Time Series; Sentiment Analysis