Financial News Sentiment Classification: A Comparative Evaluation of Lexicon-Based and Machine Learning Approaches
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Abstract
Financial news sentiment classification can support the extraction of market-relevant information from unstructured financial text. This study compares a general-purpose lexicon-based method, Valence Aware Dictionary and Sentiment Reasoner (VADER), with three supervised classifiers: Random Forest, Multinomial Naive Bayes, and linear Support Vector Machine (SVM). The experiments use 4,846 labelled statements from the Financial PhraseBank dataset to classify sentiment into positive, neutral, and negative categories. The supervised classifiers use term frequency–inverse document frequency (TF-IDF) features and an 80:20 train-test split, while VADER serves as a rule-based baseline. Based on the reported evaluation, SVM achieves the highest accuracy (0.76) and macro F1-score (0.69), followed by Random Forest, with an accuracy of 0.75 and a macro F1-score of 0.66. VADER yields the lowest reported performance, with an accuracy of 0.55 and a macro F1-score of 0.48. The confusion matrix indicates that positive and negative statements are frequently misclassified as neutral. These findings suggest that supervised classifiers provide stronger sentiment-classification performance than the general-purpose lexicon under the evaluated conditions. However, the results do not establish direct improvements in financial-risk forecasting, and further validation is required to assess generalisability across datasets and market conditions
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How to Cite
Zuhri, H. F., Noor Azma Ismail, Wahit, F., & Amalia, A. (2026). Financial News Sentiment Classification: A Comparative Evaluation of Lexicon-Based and Machine Learning Approaches. International Journal of Basic and Applied Science, 15(2), 86–94. https://doi.org/10.35335/ijobas.v8i2.873
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[27] R. Yacouby and D. Axman, “Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models,” in Proceedings of the first workshop on evaluation and comparison of NLP systems, 2020, pp. 79–91. doi: https://doi.org/10.18653/v1/2020.eval4nlp-1.9.
[2] H. Li, H. Gao, C. Wu, and M. A. Vasarhelyi, “Extracting financial data from unstructured sources: Leveraging large language models,” J. Inf. Syst., vol. 39, no. 1, pp. 135–156, 2025, doi: https://doi.org/10.2308/ISYS-2023-047.
[3] K. Du, F. Xing, R. Mao, and E. Cambria, “Financial sentiment analysis: Techniques and applications,” ACM Comput. Surv., vol. 56, no. 9, pp. 1–42, 2024, doi: https://doi.org/10.1145/3649451.
[4] M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-based Syst., vol. 226, no. 8, p. 107134, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107134.
[5] A. H. Shapiro, M. Sudhof, and D. J. Wilson, “Measuring news sentiment,” J. Econom., vol. 228, no. 2, pp. 221–243, 2022, doi: https://doi.org/10.1016/j.jeconom.2020.07.053.
[6] A. Sinha, S. Kedas, R. Kumar, and P. Malo, “SEntFiN 1.0: Entity‐aware sentiment analysis for financial news,” J. Assoc. Inf. Sci. Technol., vol. 73, no. 9, pp. 1314–1335, 2022, doi: https://doi.org/10.1002/asi.24634.
[7] S. R. Das, M. Donini, M. B. Zafar, J. He, and K. Kenthapadi, “FinLex: An effective use of word embeddings for financial lexicon generation,” J. Financ. Data Sci., vol. 8, no. 11, pp. 1–11, 2022, doi: https://doi.org/10.1016/j.jfds.2021.10.001.
[8] T. Bos and F. Frasincar, “Automatically building financial sentiment lexicons while accounting for negation,” Cognit. Comput., vol. 14, no. 1, pp. 442–460, 2022, doi: https://doi.org/10.1007/s12559-021-09833-w.
[9] R. Frankel, J. Jennings, and J. Lee, “Disclosure sentiment: Machine learning vs. dictionary methods,” Manage. Sci., vol. 68, no. 7, pp. 5514–5532, 2022, doi: https://doi.org/10.1287/mnsc.2021.4156.
[10] A. H. Huang, H. Wang, and Y. Yang, “FinBERT: A large language model for extracting information from financial text,” Contemp. Account. Res., vol. 40, no. 2, pp. 806–841, 2023, doi: https://doi.org/10.1111/1911-3846.12832.
[11] M. Costola, O. Hinz, M. Nofer, and L. Pelizzon, “Machine learning sentiment analysis, COVID-19 news and stock market reactions,” Res. Int. Bus. Financ., vol. 64, no. 1, p. 101881, 2023, doi: https://doi.org/10.1016/j.ribaf.2023.101881.
[12] A. M. Ardekani, J. Bertz, C. Bryce, M. Dowling, and S. C. Long, “FinSentGPT: A universal financial sentiment engine?,” Int. Rev. Financ. Anal., vol. 94, no. 7, p. 103291, 2024, doi: https://doi.org/10.1016/j.irfa.2024.103291.
[13] G. Fatouros, J. Soldatos, K. Kouroumali, G. Makridis, and D. Kyriazis, “Transforming sentiment analysis in the financial domain with ChatGPT,” Mach. Learn. with Appl., vol. 14, no. 15, p. 100508, 2023, doi: https://doi.org/10.1016/j.mlwa.2023.100508.
[14] J. Kim, H.-S. Kim, and S.-Y. Choi, “Forecasting the S&P 500 index using mathematical-based sentiment analysis and deep learning models: A FinBERT transformer model and LSTM,” Axioms, vol. 12, no. 9, p. 835, 2023, doi: https://doi.org/10.3390/axioms12090835.
[15] A. Groß-Klußmann, “Learning deep news sentiment representations for macro-finance,” Digit. Financ., vol. 6, no. 3, pp. 341–377, 2024, doi: https://doi.org/10.1007/s42521-024-00107-2.
[16] I. Almalis, E. Kouloumpris, and I. Vlahavas, “Sector-level sentiment analysis with deep learning,” Knowledge-Based Syst., vol. 258, no. 22, p. 109954, 2022, doi: https://doi.org/10.1016/j.knosys.2022.109954.
[17] M. Vicari and M. Gaspari, “Analysis of news sentiments using natural language processing and deep learning,” AI Soc., vol. 36, no. 3, pp. 931–937, 2021, doi: https://doi.org/10.1007/s00146-020-01111-x.
[18] F. Khalil and G. Pipa, “Is deep-learning and natural language processing transcending the financial forecasting? Investigation through lens of news analytic process,” Comput. Econ., vol. 60, no. 1, pp. 147–171, 2022, doi: https://doi.org/10.1007/s10614-021-10145-2.
[19] W. Khan, M. A. Ghazanfar, M. A. Azam, A. Karami, K. H. Alyoubi, and A. S. Alfakeeh, “Stock market prediction using machine learning classifiers and social media, news: W. Khan et al.,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 7, pp. 3433–3456, 2022, doi: https://doi.org/10.1007/s12652-020-01839-w.
[20] J. Bi, “Stock market prediction based on financial news text mining and investor sentiment recognition,” Math. Probl. Eng., vol. 2022, no. 1, p. 2427389, 2022, doi: https://doi.org/10.1155/2022/2427389.
[21] P. Xiao, “Stock market prediction based on financial news, text data mining, and investor sentiment analysis,” Int. J. Inf. Syst. Model. Des., vol. 15, no. 1, pp. 1–13, 2024, doi: 10.4018/IJISMD.361593.
[22] Y. Chen, G.-J. Wang, Y. Zhu, C. Xie, and G. Salah Uddin, “Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation,” Eur. J. Financ., vol. 30, no. 18, pp. 2157–2190, 2024, doi: https://doi.org/10.1080/1351847X.2024.2358940.
[23] J. Li, J. Li, X. Zhu, Y. Yao, and B. Casu, “Risk spillovers between FinTech and traditional financial institutions: Evidence from the US,” Int. Rev. Financ. Anal., vol. 71, no. 10, p. 101544, 2020, doi: https://doi.org/10.1016/j.irfa.2020.101544.
[24] A. Yadav, C. K. Jha, A. Sharan, and V. Vaish, “Sentiment analysis of financial news using unsupervised approach,” in Procedia Computer Science, 2020, vol. 167, pp. 589–598. doi: https://doi.org/10.1016/j.procs.2020.03.325.
[25] P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala, “Good debt or bad debt: Detecting semantic orientations in economic texts,” J. Assoc. Inf. Sci. Technol., vol. 65, no. 4, pp. 782–796, 2014, doi: https://doi.org/10.1002/asi.23062.
[26] C. Hutto and E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the international AAAI conference on web and social media, 2014, vol. 8, no. 1, pp. 216–225. doi: https://doi.org/10.1609/icwsm.v8i1.14550.
[27] R. Yacouby and D. Axman, “Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models,” in Proceedings of the first workshop on evaluation and comparison of NLP systems, 2020, pp. 79–91. doi: https://doi.org/10.18653/v1/2020.eval4nlp-1.9.
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