A Comprehensive Survey on Sentiment-Based Approaches in Stock Market Analysis

Mr. Digvijay Singh; Dr. Abhishek Guru; Dr. Asha Ambhaikar

doi:10.59367/wydysc19

Authors

Mr. Digvijay Singh Mats University, Raipur, Chhattisgarh, India
Dr. Abhishek Guru Associate Professor, Mats University, Raipur, Chhattisgarh, India
Dr. Asha Ambhaikar Director at MSEIT, Mats University, Raipur, Chhattisgarh, India

DOI:

https://doi.org/10.59367/wydysc19

Abstract

In recent years, sentiment analysis has emerged as a critical tool for enhancing stock market prediction by capturing investor mood and market psychology from textual data sources such as news articles, financial reports, and social media platforms. This paper presents a comprehensive survey of sentiment-based approaches in stock market analysis, highlighting the evolution, methodologies, and applications of sentiment-driven models. The study examines various techniques including traditional machine learning, deep learning, and natural language processing (NLP) methods employed to extract and quantify sentiment. It also explores the integration of sentiment signals into predictive financial models and their impact on forecasting stock price movements, volatility, and market trends. A comparative analysis of datasets, sentiment lexicons, model performance, and evaluation metrics is provided to identify existing challenges and research gaps. Furthermore, the paper discusses recent advancements such as the use of transformer-based architectures and multimodal sentiment fusion. This survey aims to serve as a foundational resource for researchers and practitioners seeking to leverage sentiment analysis in financial markets, and proposes future directions for developing more robust, explainable, and real-time sentiment-aware forecasting systems.

References

[1] Manisha Shinde-Pawar,”Formation of Smart Sentiment Analysis Technique for Big Data”,International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 12, December 2014

[2] Kim Schouten and Flavius Frasincar, “Survey on aspect-level sentiment analysis”, IEEE Transactions on Knowledge and Data Engineering, Volume: 28, Issue: 3, March 1 2016

[3] E.Van Kleef, H. C. M. Van Trijp, and P. Luning, “Consumer research in the early stages of new product development: a critical review of methods and techniques,” food quality preference, Vol. 16, no. 3, pp. 181–201, 2005.

[4] Y. Chen and J. Xie, “Online Consumer review: word-of-mouth as a new element of Marketing Communication mix,” manage. sci., Vol. 54, no. 3, pp. 477–491, 2008.

[5] Michał Skuza, Andrzej Romanowski, “Sentiment Analysis of Twitter Data within Big Data

Distributed Environment for Stock Prediction”, Proceedings of the Federated Conference on Computer Science and Information Systems pp. 1349–1354 ACSIS, Vol. 5.

[6] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” found. trends inf. retrieval, vol. 2, no. 1-2, pp. 1–135, 2008.

[7] Asmita Dhokrat, Sunil Khillare, C. Namrata Mahender, “Review on techniques and tools used for opinion mining”, international journal of computer applications technology and research volume 4– issue 6, 419 - 424, 2015

[8] Laura Cruz, Jose Ochoa, Mathieu Roche, Pascal Poncelet, “Dictionary-based Sentiment Analysis applied to specific domain using a Web Mining Approach”

[10] Indurkhya, N. & Damerau, F.J. [eds.] Liu, B., 2010 “Sentiment Analysis and subjectivity,” appeared in handbook of natural language processing. Bing l., 2012 “Sentiment analysis and opinion mining,” Morgan & Claypool publishers.

[11] M. Tsytsarau and T. Palpanas, “Survey on mining subjective data on the web,” data mining know. discovery, vol. 24, no. 3, pp. 478–514, 2012.

[12] Douglas R, Rice Christopher, “Corpus-based dictionaries for Sentiment Analysis of specialized

Vocabularies”version 0.1 September 19, 2013

[13] Reshma Bhonde, Binita Bhagwat , Sayali Ingulkar, Apeksha Pande, “Sentiment Analysis based on dictionary approach”, International Journal of Emerging Engineering Research and

Technology Volume 3, issue 1, January 2015

[15] Pravesh Kumar Singh, Mohd Shahid Husain,” Methodological Study of Opinion Mining and Sentiment Analysis Techniques”, International Journal on Soft Computing (ijsc) vol. 5, no. 1,

February 2014

[16] Phayung Meesad and Jiajia Li, King Mongkut's, “Stock Trend Prediction Relying on Text Mining and Sentiment Analysis with Tweets”

[17] VaanchithaKalyanaraman, Sarah Kazi, Rohan Tondulkar,”Sentiment Analysis on News Articles for Stocks”.

[18] Phayung Meesad and Jiajia Li,” Stock Trend Prediction Relying on Text Mining and Sentiment Analysis withTweets”

[19] Johan Bollen,Huina Mao,Xiao-Jun Zeng,” Twitter mood predicts the stock market”

[20] Feifei Xu and Vlado Keˇselj,”Collective Sentiment Mining of Microblogs in 24-hour Stock Price

MovementPrediction”, 2014 IEEE 16th Conference on Business Informatics

[21] Yahya Eru Cakra,Bayu Distiawan Trisedya,” Stock price prediction using linear regression based on sentiment analysis”, Advanced Computer Science and Information Systems (ICACSIS), 2015 International Conference.

[22] Alexander Porshnev, Ilya Redkin, Alexey Shevchenko,”Machine learning in prediction of stock market indicators based on historical dataand data from Twitter sentiment analysis”, 2013 IEEE 13th International Conference on Data Mining Workshops

[23] Addlight Mukwazvure,K.P Supreethi,”A Hybrid Approach to Sentiment Analysis of NewsComments”

[24] Phayung Meesad, Jiajia Li, “Stock trend prediction relying on text mining and sentiment analysis with tweets”,2014 4th World Congress on Information and Communication Technologies (WICT 2014)

[25] Kaihui Zhang, Lei Li, Peng Li,”Stock trend forecasting method based on sentiment analysis and system similarity model”,Strategic Technology (IFOST), 2011 6th International Forum.

1. Rajat Ahuja, Harshil Rastogi,Arpita Choudhuri,” Stock market forecast using sentiment analysis”, Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference