Large language models for aspect-based sentiment analysis in e-commerce: A review
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G., & Grave, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.74 · Cited by 1,109 · see citing works
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230975 · Cited by 637 · see citing works
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Ha, H. D., Prasad, P., Maag, A., & Alsadoon, A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.003 · Cited by 627 · see citing works
REALM: Retrieval-Augmented Language Model Pre-Training
Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020) · arXiv · doi:10.48550/arxiv.2002.08909 · Cited by 574 · see citing works
Thrun, S. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_8 · Cited by 434 · see citing works
Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey
Nazir, A., Rao, Y., Wu, L., & Sun, L. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2970399 · Cited by 381 · see citing works
Data augmentation approaches in natural language processing: A survey
Li, B., Hou, Y., & Che, W. (2022) · AI Open · doi:10.1016/j.aiopen.2022.03.001 · Cited by 357 · see citing works
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1242 · Cited by 308 · see citing works
Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., & Baz, M. (2022) · Sensors · doi:10.3390/s22114157 · Cited by 268 · 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
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165 · see citing works
Locating and Editing Factual Associations in GPT
Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1262 · Cited by 149 · see citing works
Sentiment analysis in e-commerce platforms: A review of current techniques and future directions
Huang, H., Zavareh, A. A., & Mustafa, M. B. (2023) · IEEE Access · doi:10.1109/access.2023.3307308 · Cited by 117 · see citing works
Self-citationZhang, B., Yang, H., & Liu, X.-Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4489831 · Cited by 113 · see citing works
Li, Y., Ding, H., Lin, Y., Feng, X., & Chang, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10685-z · Cited by 95 · see citing works
Self-citationDeep learning for aspect-based sentiment analysis: a review
Zhu, L., Xu, M., Bao, Y., Xu, Y., & Kong, X. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1044 · Cited by 85 · see citing works
Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis
Yadav, R. K., Jiao, L., Granmo, O., & Goodwin, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17671 · Cited by 81 · see citing works
Mughal, N., Mujtaba, G., Shaikh, S., Kumar, A., & Daudpota, S. M. (2024) · IEEE Access · doi:10.1109/access.2024.3386969 · Cited by 74 · see citing works
Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, X., Qian, B., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.16789 · Cited by 71 · see citing works
A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis
Maitama, J. Z., Idris, N. B., Abdi, A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3031217 · Cited by 68 · see citing works
Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention
Luo, L., Ao, X., Pan, F., Wang, J., Zhao, T., Yu, N., & He, Q. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/590 · Cited by 61 · see citing works
VisualWebArena: evaluating multimodal agents on realistic visual web tasks
Koh, J. Y., Lo, R., Jang, L., Duvvur, V., Lim, M., Huang, P.-Y., Neubig, G., Zhou, S., Salakhutdinov, R., & Fried, D. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.50 · Cited by 50 · see citing works
CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis
Lin, N., Fu, Y., Lin, X., Zhou, D. M., Yang, A., & Jiang, S. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3297964 · Cited by 48 · see citing works
Zhang, H., Cheah, Y., Alyasiri, O. M., & An, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10633-x · Cited by 48 · see citing works
Lu, H., Liu, T., Cong, R., Yang, J., Gan, Q., Fang, W. H., & Wu, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103917 · Cited by 47 · see citing works
Davoodi, L., Mezei, J., & Heikkilä, M. (2025) · Electronic Commerce Research · doi:10.1007/s10660-025-09948-4 · Cited by 43 · see citing works
Hellwig, N. C., Fehle, J., & Wolff, C. G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125514 · Cited by 40 · see citing works
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature
Sušnjak, T. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3561-2_14 · Cited by 40 · see citing works
EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Li, Y., Ma, S., Wang, X., Huang, S., Jiang, C., Zheng, H.-T., Xie, P., Huang, F., & Jiang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29820 · Cited by 38 · see citing works
Self-citationSpSAN: Sparse self-attentive network-based aspect-aware model for sentiment analysis
Jain, P. K., Quamer, W., Pamula, R., & Saravanan, V. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03436-x · Cited by 37 · see citing works
Generative approach to Aspect Based Sentiment Analysis with GPT Language Models
Chumakov, S., Kovantsev, A., & Surikov, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.12.030 · Cited by 35 · see citing works
Hasan, M. A., Das, S., Anjum, A., Alam, M. F., Anjum, A., Sarker, A., & Noori, S. R. H. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3edi4825svh3 · Cited by 34 · see citing works
Xu, K., Zhang, K., Li, J., Huang, W. D., & Wang, Y. (2024) · Electronics · doi:10.3390/electronics14010047 · Cited by 33 · see citing works
Kandhro, I. A., Ali, F., Uddin, M., Kehar, A., & Manickam, S. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02104-8 · Cited by 33 · see citing works
Yu, J., Zhao, Q., & Xia, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.81 · Cited by 31 · see citing works
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering
Shi, Z., Zhang, S., Sun, W., et al. (2024) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/2024.acl-long.397 · Cited by 31 · see citing works
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
Ye, F., Li, S., Zhang, Y., & Chen, L. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.678 · Cited by 27 · see citing works
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis
Ding, X., Zhou, J., Dou, L., Chen, Q., Wu, Y., Chen, A., & He, L. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.252 · Cited by 24 · see citing works
LLaMA-Based Models for Aspect-Based Sentiment Analysis
Šmíd, J., Přibáň, P., & Král, P. (2025) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis · doi:10.18653/v1/2024.wassa-1.6 · Cited by 24 · see citing works
Cross-lingual aspect-based sentiment analysis: A survey on tasks, approaches, and challenges
Šmíd, J., & Král, P. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103073 · Cited by 20 · see citing works
UserAdapter: Few-Shot User Learning in Sentiment Analysis
Zhong, W., Tang, D., Wang, J., Yin, J. P., & Duan, N. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.129 · Cited by 17 · see citing works
Large language models for aspect-based sentiment analysis
Simmering, P. F., & Huoviala, P. (2023) · arXiv · doi:10.48550/arxiv.2310.18025 · Cited by 17 · see citing works
On the Limitations and Prospects of Machine Unlearning for Generative AI
Zhou, S.-J., Wang, L.-Z., Ye, J., Wu, Y.-L., & Chang, H. (2024) · arXiv.org · doi:10.48550/arxiv.2408.00376 · Cited by 16
Yang, H., Zheng-ming, S., Zhao, Y., Liu, J., Wu, Y., & Qin, B. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18796-7 · Cited by 15 · see citing works
Suresh, S., & Banu, K. S. (2024) · IEEE Access · doi:10.1109/access.2024.3471816 · Cited by 14 · see citing works
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis
Wang, Z., Xie, Q., & Xia, R. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591940 · Cited by 13 · see citing works
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Baek, I., Chang, H., Kim, B., Lee, J., & Lee, H. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.181 · Cited by 13 · see citing works
A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study
Shah, F. A., Sabir, A., & Sharma, R. (2024) · arXiv.org · doi:10.48550/arxiv.2409.07162 · Cited by 12
Supervised and Few-Shot Learning for Aspect-Based Sentiment Analysis of Instruction Prompt
Huang, J., Cui, Y., Liu, J., & Liu, M. (2024) · Electronics · doi:10.3390/electronics13101924 · Cited by 10 · see citing works
Systematic review on aspect-based sentiment analysis in cross-domain
Santin, R. V., & Rezende, S. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11437-x · Cited by 10 · see citing works
Automating customer feedback analysis in E-commerce: A multi-Model approach
Davoodi, L., Mezei, J., Nikou, S., & Espinosa-Leal, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130865 · Cited by 9 · see citing works
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews
Jayakody, D., Malkith, A. V. A., Isuranda, K., et al. (2025) · International Journal on Advances in ICT for Emerging Regions (ICTer) · doi:10.4038/icter.v18i2.7290 · Cited by 8 · see citing works
A Soft Contrastive Learning-Based Prompt Model for Few-Shot Sentiment Analysis
Zhou, J., Zhou, J., Zhao, J., Wang, S., Shan, H., Gui, T., Zhang, Q., & Huang, X. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446983 · Cited by 7 · see citing works
Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA
Akram, A., & Sabir, A. S. (2023) · Informatica · doi:10.31449/inf.v47i9.5217 · Cited by 6 · see citing works
Sahin, I. C., & Eyüpoğlu, C. (2025) · Applied Computer Systems · doi:10.2478/acss-2025-0007 · Cited by 6 · see citing works
EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL
Han, Y., Bruggeman, R., Peper, J. J., Chehade, E. C., Marion, T. J., Ciuccarelli, P., & Moghaddam, M. (2023) · Proceedings of the Design Society · doi:10.1017/pds.2023.186 · Cited by 6 · see citing works
Few-Shot Methods for Aspect-Level Sentiment Analysis
Wawer, A. (2024) · Information · doi:10.3390/info15110664 · Cited by 5 · see citing works
Word-wise Sparse Attention for Multimodal Sentiment Analysis
Fan, Q., Song, H., & Han, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-532 · Cited by 5 · see citing works
A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Silcenco, O., Machado, M. R., Ugulino, W. C., & Braun, D. (2025) · International Conference on Natural Language and Speech Processing · doi:10.48550/arxiv.2508.17994 · Cited by 5
Aligning LLMs with Human Instructions and Stock Market Feedback in Financial Sentiment AnalysisPreprint
Zhao, Z., & Welsch, R. E. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.14926 · Cited by 3 · see citing works
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models
Vorakitphan, V., Basic, M., & Leroy-Meline, G. (2024) · arXiv.org · doi:10.48550/arxiv.2407.04050 · Cited by 3
Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs
Atwal, T., Tieu, C. N., Yuan, Y.-F., Shi, Z., Liu, Y.-H., & Cheng, L. (2025) · arXiv.org · doi:10.48550/arxiv.2507.18055 · Cited by 3
Davoodi, L. (2023) · Bled eConference · doi:10.18690/um.fov.6.2023.15 · Cited by 2 · see citing works
Contextual Experience Replay for Self-Improvement of Language Agents
Liu, Y., Si, C., Narasimhan, K., et al. (2025) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/2025.acl-long.694 · Cited by 2 · see citing works
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Koebler, A., Thon, I., & Buettner, F. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00085 · Cited by 2 · see citing works
CoME: An Unlearning-based Approach to Conflict-free Model Editing
Jung, D., Seo, J., Lee, J., Park, C., & Lim, H. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.325 · Cited by 2 · see citing works
Uncertainty Calibration for Tool-Using Language Agents
Liu, H., Dou, Z.-Y., Wang, Y., Peng, N., & Yue, Y. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.978 · Cited by 1 · see citing works
ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification
Lee, H., Oh, S., Kim, J., Shin, J., & Tack, J. (2025) · arXiv · doi:10.48550/arxiv.2502.14565
TAXI: Evaluating Categorical Knowledge Editing for Language Models
Powell, D., Gerych, W., & Hartvigsen, T. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.906
Graph-based Confidence Calibration for Large Language ModelsPreprint
Li, Y., Wang, S., Huang, L., & Liu, L. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2411.02454
Self-citationInstruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Varia, S., Wang, S., Halder, K., Vacareanu, R., Ballesteros, M., Benajiba, Y., John, N. A., Anubhai, R., Muresan, S., & Roth, D. (2023) · Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis · doi:10.18653/v1/2023.wassa-1.3
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends
Hua, Y. C., Denny, P., Taskova, K., & Wicker, J. (2023) · arXiv · doi:10.48550/arxiv.2311.10777
Explainable Sentiment Analysis With DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
Huang, D., & Wang, Z. (2025) · IEEE Intelligent Systems · doi:10.1109/mis.2025.3614967
Hu, J., Huang, Z., Yin, X., Ruan, W., Cheng, G., Dong, Y., & Huang, X. (2025) · Advances in Neural Information Processing Systems 38 · doi:10.52202/085713-3419
SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents
Tian, W., Zhang, S., Zhang, K., Chi, X., Fan, C.-K., Lu, J., Luo, Y., Zhou, Q., Zhao, Y., Liu, N., et al. (2025) · Advances in Neural Information Processing Systems 38 · doi:10.52202/085713-2628
Fuzzy rule based systems for interpretable sentiment analysis
Liu, H., & Cocea, M. (2017) · 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2017.7974497
Flagged first, then by citations
Research works printed but not linked: 19
- Verma, 2022, A Surv. Cross-Ling. Asp. -Based Sentim. Anal.
- Huang, 2024, Supervised and Few-Shot Learning for Aspect-Based Sentiment Analysis of Instruction Prompt, Electronics, 13, 1924, 10.3390/electronics13101924 · DOI
- Lewis, 2020, Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems (NeurIPS 2020), Curran Assoc. Inc.
- Merth, 2024, Superposition prompting: Improving and accelerating retrieval-augmented generation, arXiv Prepr. arXiv, 06910
- Singh, 2018, Natural language processing for information extraction, arXiv Prepr. arXiv, 02383
- Thogesan, 2025, Integration of explainable ai techniques with large language models for enhanced interpretability for sentiment analysis, arXiv Prepr. arXiv, 11948
- Beltagy, 2020, Longformer: The long-document transformer, arXiv
- Fedus, 2021, Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, arXiv
- Zaheer, 2020, Big Bird: Transformers for longer sequences. In Advances in Neural Information Processing Systems (NeurIPS 2020), arXiv
- Xiong, 2023, Effective long-context scaling of foundation models, arXiv Prepr. arXiv, 16039
- Shazeer, 2017, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, arXiv
- Wang, 2024, Model compression and efficient inference for large language models: A survey, arXiv Prepr. arXiv, 09748
- Wei, 2024, Stable knowledge editing in large language models, arXiv Prepr. arXiv, 13048
- Wu, 2023, Eva-kellm: A new benchmark for evaluating knowledge editing of llms, arXiv Prepr. arXiv, 09954
- Zhang, 2024, A comprehensive study of knowledge editing for large language models, arXiv Prepr. arXiv, 01286
- Nguyen, 2022, A survey of machine unlearning, arXiv
- Veldanda, 2024, Llm surgery: Efficient knowledge unlearning and editing in large language models, arXiv Prepr. arXiv, 13054
- Xu, 2024, Lmeraser: Large model unlearning through adaptive prompt tuning, arXiv Prepr. arXiv, 11056
- Drouin, 2024, Workarena: How capable are web agents at solving common knowledge work tasks?, arXiv Prepr. arXiv, 07718
Cited by
Still being checked.
- Authors
- Huang, H., Li, Y., Dong, Q., Asemi, A., Mustafa, M. B., & Asemi, A.
- Affiliations
- University of Malaya (MY)
- Illinois Institute of Technology (US)
- Corvinus University of Budapest (HU)
- Corresponding author
- Asemi, Adeleh
- Mustafa, Mumtaz Begum
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2026-11
- Volume
- 203
- Pages
- 116083
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W7170766035
- Cited by
- 1
- References
- 80
- Citations by year
- 2026: 1
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Applied Soft Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- aspect-based sentiment analysis, large language models, online customer reviews, customer opinion mining, encoder-only models, decoder-only models, encoder-decoder architecture, few-shot learning, retrieval-augmented generation, domain adaptation, multilingual sentiment analysis, hallucinations, factual grounding, multimodal sentiment analysis
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