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International Journal of Human Resource & Industrial Research

PREDICTING STOCK MARKET TRENDS IN INDIA USING SENTIMENT ANALYSIS: A STUDY OF THE 2019 LOK SABHA ELECTIONS

Dr. Kajal Gandhi

Dr. Kajal Gandhi, Assistant Professor, Department of Commerce, Shri Shikshayatan College, Kolkata, India

DOI : http://doi.org/10.5281/zenodo.14869118 Page No : 20-29

Published Online : 2022-12-30

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Predicting stock market trends has traditionally posed significant challenges, particularly in a dynamic and rapidly growing market like India. This study investigates the potential of sentiment analysis as a predictive tool for the Indian stock market, focusing on sentiment data collected during the 2019 Lok Sabha elections. By analyzing textual data from Twitter, financial news, and corporate disclosures, the study explores how public sentiment correlates with stock price movements on the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE). Leveraging machine learning and deep learning models, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and BERT, sentiment data is categorized as positive, neutral, or negative. The analysis demonstrates that while negative sentiment often aligns with short-term stock price declines, positive sentiment does not consistently drive price increases, suggesting an asymmetry in sentiment's market impact. The findings highlight the potential value of sentiment analysis as a complementary tool for stock trend prediction in India, particularly during periods of heightened public interest, such as elections. This research contributes to financial sentiment analysis, offering insights for investors, analysts, and policymakers to make more informed decisions in the Indian stock market.

Keywords: sentiment analysis, Indian stock market, stock trend prediction, Lok Sabha elections, Machine learning, public sentiment, BSE, NSE

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Cite this Article as

Gandhi K. (2022), “Predicting Stock Market Trends In India Using Sentiment Analysis: A Study of The 2019 Lok Sabha Elections”, International Journal of Human Resource & Industrial Research, Volume 9, Issue 2, 2022, pp 20-29. DOI : http://doi.org/10.5281/zenodo.14869118

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REFERENCES

Ameur, H., & Jamoussi, S. (2016). Dynamic construction of dictionaries for sentiment classification. IEEE 13th International Conference on Data Mining Workshops, 896–903.

Arias et al., (2021). Impacts of climate change and vegetation response on future aridity in a Mediterranean catchment. DOI: https://doi.org/10.1016/j.agwat.2024.108878

Batool, R., Khattak, A. M., Maqbool, J., & Lee, S. (2013). Precise tweet classification and sentiment analysis. IEEE 12th International Conference on Computer and Information Science, 461–466.

Bollen et al. (2011). Twitter mood predicts the stock market. Journal of Computational Science. DOI: https://doi.org/10.1016/j.jocs.2010.12.007

Dash, S. (2016). Marketing Research: An Applied Orientation. Pearson. Retrieved From: https://www.scirp.org/reference/referencespapers?referenceid=3270266

Gupta et al. (2019). A Response to Gupta et al. (2019) Regarding the MoT3 Wheat Blast Diagnostic Assay. DOI: https://doi.org/10.1094/phyto-10-18-0397-le

Kechaou, Z., Ben Ammar, M., & Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users' opinions. IEEE International Conference on Global Engineering Education, 1032–1038.

Lu, Y., & Chen, J. (2014). Public opinion analysis of micro blog content. IEEE International Conference on Information Science and Applications, 1–5.

Meral, M., & Diri, B. (2014). Sentiment analysis on Twitter. IEEE 22nd International Conference on Signal Processing and Communications Applications Conference, 690–693.

Mittal and Goel (2012). Stock Prediction Using Twitter Sentiment Analysis. Retrieved From: https://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf

Molla, A., Biadgie, Y., & Sohn, K. A. (2014). Network-based visualization of opinion mining and sentiment analysis on Twitter. IEEE International Conference on IT Convergence and Security, 1–4.

Rao & Murthy, (2017). Dln Murthy Rao Oration: Child Sexual Abuse In Indian Context: Fact Or Fiction. DOI: https://doi.org/10.4103/0019-5545.197001

Sun, B., & Ng, V. T. (2014). Analyzing sentimental influence of posts on social networks. IEEE International Conference on Computer Supported Work in Design, 546–551.

Wang, X., & Luo, X. (2015). Sentimental space-based analysis of user personalized sentiments. IEEE 9th International Conference on Semantics, Knowledge and Grids, 151–156.

Wu, Y., & Ren, F. (2011). Learning sentimental influence in Twitter. IEEE International Conference on Future Computer Sciences and Applications, 119–122.

Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through Twitter. Elsevier Procedia-Social and Behavioural Sciences, 55–62.

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