Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling
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Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling

Tai Zhang 1* Ziheng Wei 2, Haoxuan Wu 3, Yuxuan Chang 4
1 Artificial Intelligence, Tianjin University, China
2 Software Engineering, South China Normal University, China
3 Information Management and Information Systems, South China Normal University, China
4 Aberdeen Institute Of Data Science And Artificial Intelligence, Huijia private school, Beijing, 102200, China
*Corresponding author: 1535833950@qq.com
Published on 19 November 2025
Volume Cover
ACE Vol.207
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-539-4
ISBN (Online): 978-1-80590-540-0
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Abstract

In this study, we aim to predict stock market trends based on news using a special type of AI model to confront with the chaos of the stock market and the limitations of traditional models that ignore public opinion. We developed a business-oriented sentiment labeling system based on a standard sentiment labeling system and real financial logic, which can achieve 89.0% classification accuracy. On this basis, we constructed an improved prediction model trained on multiple financial data (transaction, fundamental, news, etc.). The method is to directly integrate and test. The results show that the model has a good prediction effect, and the R^2 value on the test set is 0.80. The experimental results show that, compared with the model without news sentiment features, the model with news sentiment features is more likely to be improved, especially when the data scale is large. This work proves that the news-based artificial intelligence model with business logic can improve the prediction effect of finance and quantitative trading.

Keywords:

Stock market prediction, news sentiment, multi-dimensional data, artificial intelligence, machine learning, financial forecast, feature fusion, quantitative trading

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Zhang,T.;Wei,Z.;Wu,H.;Chang,Y. (2025). Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling. Applied and Computational Engineering,207,61-66.

References

[1]. arXiv: 2412.19245 [q-fin.CP] https: //doi.org/10.48550/arXiv.2412.19245

[2]. arXiv: 2502.05186 [q-fin.ST] https: //doi.org/10.48550/arXiv.2502.05186

[3]. Allen, D. E., McAleer, M., & Singh, A. K. (2015, July). Daily market news sentiment and stock prices (Working Paper No. 1511). Instituto Complutense de Análisis Económico; Centre for Applied Financial Studies, University of South Australia; Department of Quantitative Finance, National Tsing Hua University. https: //www.ucm.es/fundamentos-analisis-economico2/documentos-de-trabajo-del-icae

[4]. Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the Relationship Between Financial News and the Stock Market. Scientific Reports, 3, 3578. https: //doi.org/10.1038/srep03578

[5]. Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the relationship between financial news and the stock market. Scientific Reports, 3(3578), 1-6. https: //doi.org/10.1038/srep03578

[6]. Gidófalvi, G. (2001, June 15). Using news articles to predict stock price movements [Working Paper]. Department of Computer Science and Engineering, University of California, San Diego.

[7]. Wu, S. T., Liu, Y. L., Zou, Z. R., & Weng, T. H. (2022). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44-62. https: //doi.org/10.1080/09540091.2021.1940101

[8]. Soni, P., Tewari, Y., & Krishnan, D. (2022). Machine Learning Approaches in Stock Price Prediction: A Systematic Review. Journal of Physics: Conference Series, 2161, 012065. https: //doi.org/10.1088/1742-6596/2161/1/012065

[9]. Singh, R., & Srivastava, S. (2016). Stock prediction using deep learning. Multimedia Tools and Applications, 76(11), 13373-13394. https: //doi.org/10.1007/s11042-016-4159-7

[10]. Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (n.d.). A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques. Journal of Physics: Conference Series

[11]. arXiv: 2503.03612 [q-fin.ST] https: //doi.org/10.48550/arXiv.2503.03612

[12]. arXiv: 2410.07143 [q-fin.ST] https: //doi.org/10.48550/arXiv.2410.07143

[13]. Zhang, B., Yang, H. Y., Zhou, T., Babar, A., & Liu, X. Y. (2023). Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the 4th ACM International Conference on AI in Finance (ICAIF 2023). arXiv: 2310.04027v2 [cs.CL].

[14]. Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models [Master’s Thesis]. Faculty of Science, University of Amsterdam. arXiv: 1908.10063v1 [cs.CL].

[15]. arXiv: 2309.00618 [q-fin.TR] https: //doi.org/10.48550/arXiv.2309.00618

References

[1]. arXiv: 2412.19245 [q-fin.CP] https: //doi.org/10.48550/arXiv.2412.19245

[2]. arXiv: 2502.05186 [q-fin.ST] https: //doi.org/10.48550/arXiv.2502.05186

[3]. Allen, D. E., McAleer, M., & Singh, A. K. (2015, July). Daily market news sentiment and stock prices (Working Paper No. 1511). Instituto Complutense de Análisis Económico; Centre for Applied Financial Studies, University of South Australia; Department of Quantitative Finance, National Tsing Hua University. https: //www.ucm.es/fundamentos-analisis-economico2/documentos-de-trabajo-del-icae

[4]. Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the Relationship Between Financial News and the Stock Market. Scientific Reports, 3, 3578. https: //doi.org/10.1038/srep03578

[5]. Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the relationship between financial news and the stock market. Scientific Reports, 3(3578), 1-6. https: //doi.org/10.1038/srep03578

[6]. Gidófalvi, G. (2001, June 15). Using news articles to predict stock price movements [Working Paper]. Department of Computer Science and Engineering, University of California, San Diego.

[7]. Wu, S. T., Liu, Y. L., Zou, Z. R., & Weng, T. H. (2022). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44-62. https: //doi.org/10.1080/09540091.2021.1940101

[8]. Soni, P., Tewari, Y., & Krishnan, D. (2022). Machine Learning Approaches in Stock Price Prediction: A Systematic Review. Journal of Physics: Conference Series, 2161, 012065. https: //doi.org/10.1088/1742-6596/2161/1/012065

[9]. Singh, R., & Srivastava, S. (2016). Stock prediction using deep learning. Multimedia Tools and Applications, 76(11), 13373-13394. https: //doi.org/10.1007/s11042-016-4159-7

[10]. Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (n.d.). A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques. Journal of Physics: Conference Series

[11]. arXiv: 2503.03612 [q-fin.ST] https: //doi.org/10.48550/arXiv.2503.03612

[12]. arXiv: 2410.07143 [q-fin.ST] https: //doi.org/10.48550/arXiv.2410.07143

[13]. Zhang, B., Yang, H. Y., Zhou, T., Babar, A., & Liu, X. Y. (2023). Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the 4th ACM International Conference on AI in Finance (ICAIF 2023). arXiv: 2310.04027v2 [cs.CL].

[14]. Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models [Master’s Thesis]. Faculty of Science, University of Amsterdam. arXiv: 1908.10063v1 [cs.CL].

[15]. arXiv: 2309.00618 [q-fin.TR] https: //doi.org/10.48550/arXiv.2309.00618

Cite this article

Zhang,T.;Wei,Z.;Wu,H.;Chang,Y. (2025). Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling. Applied and Computational Engineering,207,61-66.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-539-4(Print) / 978-1-80590-540-0(Online)
Editor: Marwan Omar, Guozheng Rao
Conference website: https://www.confspml.org/tianjin.html
Conference date: 21 December 2025
Series: Applied and Computational Engineering
Volume number: Vol.207
ISSN: 2755-2721(Print) / 2755-273X(Online)

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