Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Business and Policy Studies
Series Vol. 9 , 13 September 2023
Open
Access | Article
Analysis on Text Mining in Stock Market Applications
* Author to whom correspondence should be addressed.
Abstract
Stocks are a financial product with high risk but high reward and flexible trading that many investors prefer. If an investor can accurately predict the price of a stock, he or she will be rewarded handsomely. Stock prices, on the other hand, are influenced by a variety of factors, including macroeconomic conditions, market conditions, major socioeconomic events, investor preferences, and company business decisions. As a result, stock price forecasting has become the focus and difficulty of research in a variety of fields. Stock price prediction entails gathering news and commentaries, analyzing historical data, and determining the impact of news events on investor sentiment and stock price trends. The purpose of this paper is to provide an introduction to the application of text mining in the stock market, including commonly used text mining and prediction models, as well as to highlight problems in the field and suggest some future directions for improvement or research. Finally, many unresolved issues are raised in order to contribute to future research in this area.
Keywords
text mining, stock market prediction, SVM, LSTM, NB, machine learning
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-915371-45-4
- ISBN (Online)
- 978-1-915371-46-1
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/9/20230353
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited