Designing Heterogeneous LLM Agents for Financial Sentiment Analysis
Open accessAbstract
Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focus from massive data acquisition and new model training to human alignment and strategic elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA) due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage existing generative models in such a context. This study investigates the effectiveness of the new paradigm, that is, using LLMs without fine-tuning for FSA. Rooted in Minsky’s theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed and applied to FSA. The framework instantiates specialized agents using prior guiding knowledge from both linguistics and finance. Then, a summative agent reasons on the aggregated agent discussions. Comprehensive evaluations using six FSA datasets show that the framework yields better accuracies compared to many alternative multi-LLM agent settings, especially when the discussion contents are substantial. This study contributes to the design foundations and paves new avenues for LLMs-based FSA and potentially other tasks. Implications for business and management have also been discussed.
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A heterogeneous debating framework where LLM agents with different roles debate with each other improves news sentiment classification performance.
“[51] proposed a heterogeneous debating framework where LLM agents with different roles (i.e. mood agent, rhetoric agent, dependency agent, etc.) debate with each other, which improves the news sentiment classification performance.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [51]
News data from reputable sources is extensively used in various studies to stay up-to-date with the real-world financial market.
“This type of data is extensively used in various studies [20, 27, 51, 53, 57] to stay up-to-date with the real-world financial market.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [51]
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Design Science in Information Systems Research1
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004) · MIS Quarterly · doi:10.2307/25148625 · Cited by 10,707 · cited 2× · see citing works
“Hevner et al. [18] described five kinds of design evaluation methods.”
MethodWhen Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks
LOUGHRAN, T., & MCDONALD, B. (2011) · The Journal of Finance · doi:10.1111/j.1540-6261.2010.01625.x · Cited by 6,015 · see citing works
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · cited 1× · see citing works
“The usefulness and the importance of accurate FSA are also underpinned by a long thread of research [1, 37, 9, 5].”
BackgroundLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 2× · see citing works
“To aggregate information, a widely used technique is to concatenate them into a longer prompt [35, 13, 17, 21].”
MethodResultLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 2× · see citing works
“To aggregate information, a widely used technique is to concatenate them into a longer prompt [35, 13, 17, 21].”
MethodResultPositioning and Presenting Design Science Research for Maximum Impact1
Gregor, S. D., & Hevner, A. R. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.2.01 · Cited by 3,154 · see citing works
The Behavior of Individual Investors
Barber, B. M., & Odean, T. (2013) · Handbook of the Economics of Finance · doi:10.1016/b978-0-44-459406-8.00022-6 · Cited by 750 · see citing works
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639 · cited 1× · see citing works
“In terms of designing prompts, Liu and Chilton [22] studied text-to-image generative models and the prompt template “SUBJECT in the style of STYLE".”
BackgroundGood debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513 · see citing works
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Zhang, W., Deng, Y., Liu, B., Pan, S. J., & Bing, L. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.246 · Cited by 377 · see citing works
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.891 · Cited by 361 · see citing works
BloombergGPT: A Large Language Model for Finance
Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., & Mann, G. (2023) · arXiv · Cited by 357 · see citing works
FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining
Liu, Z., Huang, D., Huang, K., Li, Z., & Zhao, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/622 · Cited by 327 · see citing works
Measuring Massive Multitask Language Understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2020) · arXiv · doi:10.48550/arxiv.2009.03300 · Cited by 291 · see citing works
Durcikova, A., Lee, A. S., & Brown, S. A. (2017) · MIS Quarterly · doi:10.25300/misq · Cited by 255 · see citing works
The State-of-the-Art in Twitter Sentiment Analysis
Zimbra, D. K., Abbasi, A., Zeng, D. D., & Chen, H. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3185045 · Cited by 244 · see citing works
Leveraging Financial Social Media Data for Corporate Fraud Detection
Dong, W. P., Liao, S., & Zhang, Z. (2018) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1451954 · Cited by 239 · see citing works
FinTech as a Game Changer: Overview of Research Frontiers
Hendershott, T. J., Zhang, X., Zhao, J. L., & Zheng, Z. (2021) · Information Systems Research · doi:10.1287/isre.2021.0997 · Cited by 205 · see citing works
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Zhang, B., Yang, H., Zhou, T., Babar, M. A., & Liu, X.-Y. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626866 · Cited by 199 · see citing works
Financial Sentiment Analysis: Techniques and Applications
Du, K., Xing, F., Mao, R., & Cambria, E. (2024) · ACM Computing Surveys · doi:10.1145/3649451 · Cited by 196 · see citing works
Self-citationReasoning Implicit Sentiment with Chain-of-Thought Prompting
Fei, H., Li, B., Liu, Q., Bing, L., Li, F., & Chua, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.101 · Cited by 193 · see citing works
Sentiment Analysis in the Age of Generative AI
Krugmann, J. O., & Hartmann, J. (2024) · Customer Needs and Solutions · doi:10.1007/s40547-024-00143-4 · Cited by 189 · see citing works
Maia, M., Handschuh, S., Freitas, A., Davis, B., McDermott, R., Zarrouk, M., & Balahur, A. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3192301 · Cited by 178 · see citing works
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.16452 · Cited by 178 · see citing works
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Handschuh, S., & Davis, B. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2089 · Cited by 159 · see citing works
The Interaction between Microblog Sentiment and Stock Returns: An Empirical Examination1
Deng, S., Huang, Z. J., Sinha, A. P., & Zhao, H. (2018) · MIS Quarterly · doi:10.25300/misq/2018/14268 · Cited by 144 · see citing works
Intelligent Asset Allocation via Market Sentiment Views
Xing, F., Cambria, E., & Welsch, R. E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2866727 · Cited by 130 · see citing works
Self-citationCognitive-inspired domain adaptation of sentiment lexicons
Xing, F., Pallucchini, F., & Cambria, E. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2018.11.002 · Cited by 126 · see citing works
Self-citationAn Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
Sorensen, T., Robinson, J., Rytting, C., Shaw, A., Rogers, K., Delorey, A., Khalil, M., Fulda, N., & Wingate, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.60 · Cited by 113 · see citing works
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023) · arXiv · doi:10.48550/arxiv.2305.14325 · Cited by 96 · see citing works
Sentiment-aware volatility forecasting
Xing, F., Cambria, E., & Zhang, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.03.029 · Cited by 81 · see citing works
Self-citationFinancial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
Xing, F., Malandri, L., Zhang, Y., & Cambria, E. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.85 · Cited by 79 · see citing works
Self-citationSEntFiN 1.0: Entity‐aware sentiment analysis for financial news
Sinha, A., Kedas, S., Kumar, R., & Malo, P. (2022) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24634 · Cited by 78 · see citing works
Teaching Large Language Models to Self-Debug
Chen, X., Lin, M., Schärli, N., & Zhou, D. (2023) · arXiv · doi:10.48550/arxiv.2304.05128 · Cited by 77 · cited 1× · see citing works
“LLM collaboration is also more investigated on many general natural language processing tasks including sentiment analysis, but their applicability on FSA lacks direct evidence.”
When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain
Shah, R., Chawla, K., Eidnani, D., Shah, A., Du, W., Chava, S., Raman, N., Smiley, C., Chen, J., & Yang, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.148 · Cited by 74 · see citing works
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03915 · Cited by 61 · cited 1× · see citing works
“Either way, the observed performances suggest that heterogeneous agents have complicated non-linear interactions, and the presented design can be further optimized with more empirical evidence.”
Deep Learning for Information Systems Research
Samtani, S., Zhu, H., Padmanabhan, B., Chai, Y., Chen, H., & Nunamaker, J. F. (2023) · Journal of Management Information Systems · doi:10.1080/07421222.2023.2172772 · Cited by 61 · see citing works
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis
Du, K., Xing, F., & Cambria, E. (2023) · ACM Transactions on Management Information Systems · doi:10.1145/3580480 · Cited by 50 · see citing works
Self-citationYang, Y., Qin, Y. W., Fan, Y., & Zhang, Z. (2023) · MIS Quarterly · doi:10.25300/misq/2022/17062 · Cited by 48 · see citing works
Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks
Yu, F., Quartey, L., & Schilder, F. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.858 · Cited by 47 · see citing works
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis
Deng, X., Bashlovkina, V., Han, F., Baumgartner, S., & Bendersky, M. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587324 · Cited by 46 · see citing works
Leveraging hierarchical language models for aspect-based sentiment analysis on financial data
Lengkeek, M., van der Knaap, F., & Frăsincar, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103435 · Cited by 41 · see citing works
Investor Sentiment and Paradigm Shifts in Equity Return Forecasting
Chu, L., He, X.-Z. '., Li, K., & Tu, J. (2022) · Management Science · doi:10.1287/mnsc.2020.3834 · Cited by 36 · see citing works
Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods
Cesarini, M., Malandri, L., Pallucchini, F., Seveso, A., & Xing, F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10325-w · Cited by 36 · see citing works
Peterson, R. L. (2016) · Wiley eBooks · doi:10.1002/9781119219149 · Cited by 36 · see citing works
Large Language Models for Conducting Advanced Text Analytics Information Systems Research
Ampel, B., Yang, C.-H., Hu, J. L., & Chen, H. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3682069 · Cited by 31 · see citing works
An Evaluation of Reasoning Capabilities of Large Language Models in Financial Sentiment Analysis
Du, K., Xing, F., Mao, R., & Cambria, E. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00042 · Cited by 24 · see citing works
Self-citationLarge Language Model Adaptation for Financial Sentiment Analysis
Inserte, P. R., Nakhlé, M., Qader, R., Caillaut, G., & Liu, J. (2023) · Workshop on Financial Technology and Natural Language Processing (FinNLP) · doi:10.18653/v1/2023.finnlp-2.1 · Cited by 21 · see citing works
FinEntity: Entity-level Sentiment Classification for Financial Texts
Tang, Y., Yang, Y., Huang, A., Tam, A., & Tang, J. E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.956 · Cited by 17 · see citing works
Costa, D. D. F., & da Silva, N. F. F. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191828 · Cited by 14 · see citing works
Sentiment Analysis through LLM NegotiationsPreprint
Sun, X., Li, X., Zhang, S., Wang, S., Wu, F. Q., Li, J., Zhang, T., & Wang, G. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.01876 · Cited by 14 · see citing works
Social Trading, Communication, and Networks
Deng, J., Yang, M.-W., Pelster, M., & Tan, Y. (2023) · Information systems research · doi:10.1287/isre.2021.0143 · Cited by 13 · see citing works
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Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., & Yang, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11236-4 · Cited by 186 · see citing works
A review of sentiment analysis: tasks, applications, and deep learning techniques
Sharma, N. A., Ali, A. B. M. S., & Kabir, M. A. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00594-x · Cited by 145 · see citing works
Natural language processing in finance: A survey
Du, K., Zhao, Y., Mao, R., Xing, F., & Cambria, E. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102755 · Cited by 76 · see citing works
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Lee, D. K. C., Guan, C., Yu, Y., & Ding, Q. (2024) · FinTech · doi:10.3390/fintech3030025 · Cited by 61 · see citing works
Bhattacharya, M., Pal, S., Chatterjee, S., Lee, S., & Chakraborty, C. (2024) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2024.102255 · Cited by 38 · see citing works
Harnessing the Power of LLMs for Service Quality Assessment From User-Generated Content
Falatouri, T. N., Hrušecká, D., & Fischer, T. (2024) · IEEE Access · doi:10.1109/access.2024.3429290 · Cited by 31 · see citing works
Fatemi, S., Hu, Y., & Mousavi, M. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3706119 · Cited by 33 · see citing works
The contribution of GenAI to business analytics
Salazar, A. J., & Kunc, M. H. (2025) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2435835 · Cited by 27 · see citing works
Nasiopoulos, D. Κ., Roumeliotis, K. I., Σακάς, Δ. Π., Toudas, K. S., & Reklitis, P. (2025) · International Journal of Financial Studies · doi:10.3390/ijfs13020075 · Cited by 24 · see citing works
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Nip, J. Y. M., & Berthelier, B. (2024) · Encyclopedia · doi:10.3390/encyclopedia4040104 · Cited by 35 · see citing works
Human-Artificial Intelligence in Management Functions: A Synergistic Symbiosis Relationship
Islami, X., & Mulolli, E. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2439615 · Cited by 34 · see citing works
Large Language Models in equity markets: applications, techniques, and insights
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de Curtò, J., & de Zarzà, I. (2025) · IEEE Access · doi:10.1109/access.2025.3548451 · Cited by 21 · see citing works
Jagged competencies: Measuring the reliability of generative AI in academic research
Thomas, L. D. W., Romasanta, A. K. S., & Priego, L. P. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115804 · Cited by 19 · see citing works
LLM-infused bi-level semantic enhancement for corporate credit risk prediction
Lu, S., Su, Y., Zhang, X., Chai, J., & Yu, L. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104091 · Cited by 14 · see citing works
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Saxena, A., Santhanavijayan, A., Shakya, H. K., Kumar, G., Balusamy, B., & Benedetto, F. (2024) · Mathematics · doi:10.3390/math12213332 · Cited by 13 · see citing works
Graph-aware pre-trained language model for political sentiment analysis in Filipino social media
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Empowered stock market forecasting using Large Language Model on social media content
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Cui, T., Ye, Y., Li, Y., Du, N., Song, X., Zhu, Y., Yang, X., & Štrbac, G. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100562 · Cited by 6 · see citing works
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Batra, D., Hamill, C. B., Hartley, J., Okhrati, R., Seddon, D., Miller, H., Khraishi, R., & Cowan, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5381584 · Cited by 6 · see citing works
Gao, X., & Fang, Q. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00459-7 · Cited by 4 · see citing works
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Zhang, D. (2025) · Transportation · doi:10.1007/s11116-024-10578-2 · Cited by 2 · see citing works
Yousuf, M., Rifat, M. H., Mondal, P. K., Arpan, A. P., Islam, M. M., & Rahman, M. S. (2025) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce64574.2025.11013469 · Cited by 2 · see citing works
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Dai, Y., Liao, M., & Li, Z. (2024) · Highlights in Business Economics and Management · doi:10.54097/4rwdat95 · Cited by 1 · see citing works
Generative AI-Based Framework for Advancing Pitch Deck Evaluation of Sustainability Ventures
Kannan, S., Eshwar, D., Chandrasekaran, G., Bhowmick, B., & Kumar, C. S. (2026) · International Journal of Global Business and Competitiveness · doi:10.1007/s42943-025-00141-w · Cited by 1 · see citing works
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Tan, C., Li, X., Qu, Y., Chen, C., Cui, S., & Gao, L. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3660137 · Cited by 1 · see citing works
Chiang, T.-M., Lee, M.-C., Tsai, Z.-R., & Fan, Z. (2026) · Applied Economics Letters · doi:10.1080/13504851.2026.2662546 · Cited by 1 · see citing works
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zhang, H., & Rajan, R. E. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--56701
Opitz, D., & Hamm, A. (2025) · ACM Symposium on Document Engineering (DocEng) · doi:10.1145/3704268.3748678
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Tan, S., So, C. C., Sun, Y., Wang, J., Loh, A. W.-K., & Yung, S. P. (2025) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3768292.3770368
Oh, D., Kim, T., Jang, J., & Park, S.-H. (2025) · International Conference on AI in Finance · doi:10.1145/3768292.3770376 · Cited by 3 · see citing works
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de Armas, E. M., Campos, J. G., da Silva, G. M. H., Santos, P. I. N., Corseuil, E. T., & de Lima, D. T. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10721-3_8
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Dong, J., Wang, Y., Wang, S., & Chen, X. (2025) · International Conference on Machine Intelligence and Nature-Inspired Computing (MIND) · doi:10.1109/mind67540.2025.11351573
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Kang, J., & Kang, J. (2026) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.402700
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Afane, M., Satyam, A., Chen, K., Li, T., Farooq, J., & Chen, J. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402507 · Cited by 1 · see citing works
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Gauraha, R., Agrawal, A. K., & Dubey, P. (2026) · International Conference on Emerging Trends and Innovations in ICT (ICEI) · doi:10.1109/icei65890.2026.11447631
Kim, J., Suzara, M., & Mitchell, J. C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790456
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Pareek, S., Govers, J., Kollerup, N. K., Wong, E., Velloso, E., & Gonçalves, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791157
Suwanwijit, N., Viriyapant, K., & Bhumpenpein, N. (2025) · Artificial Intelligence and Cloud Computing Conference (AICCC) · doi:10.1145/3789982.3790000
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Huang, C., Yan, Y., Hong, T., & Wu, H.-C. (2026) · Investment Analysts Journal · doi:10.1080/10293523.2026.2644790 · Cited by 1 · see citing works
Reddy, M. V. G. K., Pramod, M., & Aishwarya, R. (2026) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs67901.2026.11502813
Transforming Financial Decision-Making
Bhatnagr, P. (2026) · IGI Global eBooks · doi:10.4018/979-8-3693-6690-5.ch008
Real-Time Financial Sentiment Extraction Using Large Language Model (LLM) Architectures
Palivela, L. H., Athinarapu, S., Basireddy, G. R., & Teja, T. V. K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3693-6690-5.ch006
Transforming Financial Decision-Making
Bhatnagr, P. (2026) · IGI Global eBooks · doi:10.4018/979-8-3693-6690-5.ch010
Bawack, R. E., & Carillo, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-28313-9_16
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Zhang, O., & Zhang, Z. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536649
Secure Multibranch Deep Learning Framework for Effective Stock Sentiment Analysis
Shakeel, M. R., Alam, S., Ahmad, M., & Khaja, I. A. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3691026
Zhao, Q., Yang, H., Song, Q., & Li, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133255
Jibrin, N. A., Abubakar, A., Iya, M. U., Aimufua, G., & Adenomon, M. O. (2026) · Journal of Statistical Sciences and Computational Intelligence · doi:10.64497/jssci.98
The new era in financial evolution: a review of applications of generative AI in finance
Feng, S., Yeo, E. X. Y., Ong, R., Ren, J. S. J., Tan, Y. L., Xie, M., Wang, J., & Li, F. (2026) · Memetic Computing · doi:10.1007/s12293-026-00512-7
Li, L., He, Y., Liu, L., & Zhao, J. (2026) · Decision Support Systems · doi:10.1016/j.dss.2026.114718
The Rise of Generative AI in Finance: A Survey of Techniques and Studies
Feng, S., Tan, Y. L., Ren, J. S. J., Yeo, E. X. Y., Ong, R., Xie, M., & Wang, J. (2026) · Memetic Computing · doi:10.1007/s12293-026-00513-6
Large Language Models for Sentiment Analysis in Environmental Applications: A Review
Jarraya, R., Bouaziz, S., & Ammar, B. (2026) · International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) · doi:10.1109/issatk69667.2026.11566864
Wu, X., Tsai, P., Yuan, J., Yu, M., Sun, G., & Zhang, L. (2026) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc67748.2026.11575454
Guang, J. C. R., & Shah, N. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.235
Finance and emotions: A study on investor sentiment and its implication on market movements
Trotta, A., Piluso, F., Ceraso, D., & Strano, E. (2026) · Research in International Business and Finance · doi:10.1016/j.ribaf.2026.103545
Gilbert, E. K., Bono, J. E., & Hu, L. (2026) · The Leadership Quarterly · doi:10.1016/j.leaqua.2026.101993
BASFuzz: Toward Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Xiao, M., Xiao, Y., Ji, S., Tu, J., & Zhang, P. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3843770
Analyst Logical Inconsistency and Stock Price Crash Risk: Evidence from Large Language Models
Ma, Y., Hu, Z., & Gao, Z. (2026) · International Journal of Financial Studies · doi:10.3390/ijfs14090227
Tao, L., Li, Z., Ji, Y., Liao, C.-T., Xiao, X., & Cao, X. (2026) · Symmetry · doi:10.3390/sym18091498
SentiAgent: An agentic LLM framework for context-aware and explainable sentiment analysis
Yadav, U., Thakre, B. P., & Chokhandre, S. (2026) · MethodsX · doi:10.1016/j.mex.2026.104185
- Authors
- Xing, F.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Xing, Frank
- ORCID
- Xing, Frank
- Venue
- ACM Transactions on Management Information Systems
- Published
- 2025-02-10
- Volume
- 16
- Issue
- 1
- Pages
- 1-24
- ISSN
- 2158-656X (ISSN-L)
- 2158-6578
- OpenAlex
- W4401545735
- Cited by
- 88
- References
- 52
- Citations by year
- 2026: 35 · 2025: 36 · 2024: 17
- FWCI
- 12.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Management Information Systems (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- CentAUR (University of Reading) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Singapore Ministry of Education Academic Research Fund (251RES2107)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- financial sentiment analysis, large language models, multi-agent systems, domain knowledge integration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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