Abstract
Sentiment analysis in financial text plays a crucial role in understanding market trends and predicting financial outcomes. This research investigates the impact of fine-tuning and ensemble learning techniques on the performance of Large Language Models (LLMs) for financial sentiment analysis. We focus on comparing individual fine-tuned FinBERT models and various ensemble methods, particularly stacking ensembles with different meta-learners, on the FiQA (Financial Opinion Mining and Question Answering) 2018 benchmark dataset. Our methodology involves dataset selection, model development, and rigorous evaluation using multiple metrics. The results demonstrate the effectiveness of domain-specific adaptation and the potential benefits of combining multiple models in an ensemble to improve sentiment classification performance. The stacking ensemble with a Random Forest meta-classifier achieves state-of-the-art performance on both datasets, outperforming individual fine-tuned models and other ensemble methods.
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References
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. J. Assoc. Inf. Sci. Technol. 65(4), 782–796 (2014). https://doi.org/10.1002/asi.23062
Araci, D.: FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063 (2019)
Xie, Q., et al.: BUFIN: a large-scale financial dataset for blockchain and DeFi research. arXiv preprint arXiv:2306.05443 (2023)
Nyakurukwa, K., Seetharam, Y.: Can investor sentiment predict cryptocurrency returns? Evid. Bitcoin. Sci. Afr. 20, e01596 (2023). https://doi.org/10.1016/j.sciaf.2023.e01596
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011). https://doi.org/10.1016/j.jocs.2010.12.007
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) Multiple Classifier Systems. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1
Nti, I.K., Adekoya, A.F., Weyori, B.A.: A systematic review of fundamental and technical analysis of stock market predictions. Artif. Intell. Rev. 53, 3007–3057 (2020). https://doi.org/10.1007/s10462-019-09754-z
Airnow: Monthly number of active users selected leading apps that allow for online share trading worldwide from January 2017 to July 2021, by app (in 1,000s) [Graph]. Statista (2021). https://www-statista-com.manchester.idm.oclc.org/statistics/1259822/global-etrading-app-monthly-active-users/
Chen, W., Li, M., Wang, X., Zou, Y.: FinGPT: instruction-following financial large language models. arXiv preprint arXiv:2310.15205 (2023)
Malo, P., Sinha, A., Takala, P., Korhonen, P., Wallenius, J.: FinancialPhraseBank-v1.0 (2013). https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10
Chiapudding: Kaggle Financial Sentiment. Hugging Face Datasets (2024). https://huggingface.co/datasets/chiapudding/kaggle-financial-sentiment
FinanceInc: Auditor Sentiment Fine-tuned. Hugging Face Datasets (2024). https://huggingface.co/datasets/FinanceInc/auditor-sentiment-finetuned
Zeroshot: Twitter Financial News Sentiment. Hugging Face Datasets (2024). https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment
Maia, M., et al.: WWW’18 open challenge: financial opinion mining and question answering. In: Companion Proceedings of the web Conference 2018, pp. 1941–1942. International World Wide Web Conferences Steering Committee, Geneva (2018). https://doi.org/10.1145/3184558.3192301
Chen, Z., Shang, T., Chen, Y., Zhao, L.: FinSense: an assistant system for financial journalists and investors. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3697–3711. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.emnlp-demo.9
Xie, Q., Han, W.: FinGPT: open-source financial large language models. arXiv preprint arXiv:2402.12659 (2024)
Interactive Brokers: Number of accounts of Interactive Brokers from Jan 2008 to October 2021 (in 1,000s) [Graph]. Statista (2022). https://www-statista-com.manchester.idm.oclc.org/statistics/1263318/interactive-brokers-number-accounts/
NVIDIA Corporation: NVIDIA Announces Financial Results for First Quarter Fiscal 2021. NVIDIA Newsroom (2020). https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-first-quarter-fiscal-2021
Sriram, K., Jin, H.: Tesla posts record quarterly deliveries after price cuts, up 4% from Q4. Reuters (2023). https://www.reuters.com/business/autos-transportation/tesla-misses-first-quarter-delivery-estimates-2023-04-02/
Acknowledgments
I would like to thank my project supervisor, Professor Sophia Ananiadou from the University of Manchester for mentoring my project as well as the researchers Jimin Huang and Qianqian Xie, leading the Fin AI group, for our discussions and helping with my understanding of the topic for this paper.
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Model Evaluation Python Implementation
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Tang, W.L.R. (2025). Advancing Financial Text Sentiment Analysis with Deep Learning and Ensemble Models. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XLI. SGAI 2024. Lecture Notes in Computer Science(), vol 15447. Springer, Cham. https://doi.org/10.1007/978-3-031-77918-3_12
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