M. Wankhade, C. Kulkarni, and A. C. S. Rao, “A survey on aspect-based sentiment analysis methods and challenges,” Applied Soft Computing, vol. 167, Dec. 2024, Art. no. 112249. https://doi.org/10.1016/j.asoc.2024.112249
G. S. Chauhan, R. Nahta, Y. K. Meena, and D. Gopalani, “Aspect-based sentiment analysis using deep learning approaches: A survey,” Computer Science Review, vol. 49, Aug. 2023, Art. no. 100576. https://doi.org/10.1016/j.cosrev.2023.100576
K. M. Ahmad, Q. Liu, M. M. Y. Khalil, Y. Gan, A. A. Khan, and X. Liu, “Aspect-specific parsimonious segmentation via attention-based graph convolutional network for aspect-based sentiment analysis,” Knowledge-Based Systems, vol. 300, Sep. 2024, Art. no. 112169. https://doi.org/10.1016/j.knosys.2024.112169
K. Namee, J. Polpinij, and B. Luaphol, “A hybrid approach for aspectbased sentiment analysis: A case study of hotel reviews,” Current Applied Science and Technology, vol. 23, no. 2, pp. 87–104, 2023. https://doi.org/10.55003/cast.2022.02.23.008
B. Harjo, M. Muljono, and R. Abdullah, “Attention-based sentence extraction for aspect-based sentiment analysis with implicit aspect cases in hotel review using machine learning algorithm, semantic similarity, and BERT,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 3, pp. 189–200, 2023. https://doi.org/10.22266/ijies2023.0630.15
P. N. Andono, S. Sunardi, R. A. Nugroho, and B. Harjo, “Aspect-based sentiment analysis for hotel review using LDA, semantic similarity, and BERT,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 5, pp. 232–243, 2022. https://doi.org/10.22266/ijies2022.1031.21
T. Zhao, L. Meng, and D. Song, “Multimodal aspect-based sentiment analysis: A survey of tasks, methods, challenges, and future directions,” Information Fusion, vol. 112, Dec. 2024, Art. no. 102552. https://doi.org/10.1016/j.inffus.2024.102552
S. Yang, Q. Li, D. Jang, and J. Kim, “Deep learning mechanism and big data in hospitality and tourism: Developing personalized restaurant recommendation model to customer decision-making,” International Journal of Hospitality Management, vol. 121, 2024, Art. no. 103803. https://doi.org/10.1016/j.ijhm.2024.103803
N. Mughal, G. Mujtaba, S. Shaikh, A. Kumar, and S. M. Daudpota, “Comparative analysis of deep natural networks and large language models for aspect-based sentiment analysis,” IEEE Access, vol. 12, pp. 60943–60957, Apr. 2024. https://doi.org/10.1109/ACCESS.2024.3386969
B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering – A systematic literature review,” Information and Software Technology, vol. 51, no. 1, pp. 7–15, Jan. 2009. https://doi.org/10.1016/j.infsof.2008.09.009
M. Jorayeva, A. Akbulut, C. Catal, and A. Mishra, “Machine learningbased software defect prediction for mobile applications: A systematic literature review,” Sensors, vol. 22, no. 7, Mar. 2022, Art. no. 2551. https://doi.org/10.3390/s22072551
Z. Kang, C. Catal, and B. Tekinerdogan, “Machine learning applications in production lines: A systematic literature review,” Computers & Industrial Engineering, vol. 149, Nov. 2020, Art. no. 106773. https://doi.org/10.1016/j.cie.2020.106773
S. Abro, S. Shaikh, R. Ali, S. Fatima, and H. A. Mahmood Malik, “Aspect-based sentimental analysis of hotel reviews: A comparative study,” Sukkur IBA Journal of Computing and Mathematical Sciences, vol. 4, no. 1, pp. 11–20, 2020.
N. Zhao, H. Gao, X. Wen, and H. Li, “Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis,” IEEE Access, vol. 9, pp. 15561–15570, Jan. 2021. https://doi.org/10.1109/ACCESS.2021.3052937
K. Scaria, H. Gupta, S. Goyal, S. A. Sawant, S. Mishra, and C. Baral, “InstructABSA: Instruction learning for aspect-based sentiment analysis,” arXiv preprint arXiv:2302.08624, Feb. 2023. https://doi.org/10.48550/arXiv.2302.08624
D. Jayakody, A. V. A. Malkith, K. Isuranda, V. Thenuwara, N. de Silva, S. R. Ponnamperuma, G. G. N. Sandamali, and K. L. K. Sudheera, “Instruct-DeBERTa: A hybrid approach for aspect-based sentiment analysis on textual reviews,” arXiv preprint arXiv:2408.13202, Aug. 2024. https://doi.org/10.48550/arXiv.2408.13202
L. Xu, H. Xie, S. J. Qin, F. L. Wang, and X. Tao, “Exploring ChatGPTbased augmentation strategies for contrastive aspect-based sentiment analysis,” arXiv preprint arXiv:2409.11218, Sep. 2024. https://doi.org/10.48550/arXiv.2409.11218
S. Song, “Exploring large language models for multimodal sentiment analysis: Challenges, benchmarks, and future directions,” in Proceedings of the International Conference on Multimodal Sentiment Analysis, Nov. 2024. https://doi.org/10.48550/arXiv.2411.15408
J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, Mar. 2024, Art. no. 100059. https://doi.org/10.1016/j.nlp.2024.100059
H. Yang, Z. Si, Y. Zhao, J. Liu, Y. Wu, and B. Qin, “MACSA: A multimodal aspect-category sentiment analysis dataset with multimodal fine-grained aligned annotations,” Multimedia Tools and Applications, vol. 83, pp. 81279–81297, Mar. 2024. https://doi.org/10.1007/s11042-024-18796-7
B. Zeng, L. Xie, R. Li, Y. Yao, R. Li, and H. Deng, “Aspect-aware semantic feature enhanced networks for multimodal aspect-based sentiment analysis,” The Journal of Supercomputing, vol. 81, no. 64, Oct. 2025. https://doi.org/10.1007/s11227-024-06472-4
C. Xu, M. Wang, Y. Ren, and S. Zhu, “Enhancing aspect-based sentiment analysis in tourism using large language models and positional information,” Tsinghua Science and Technology, 2024. https://doi.org/10.48550/arXiv.2409.14997
V. Agarwal, P. Aher, and V. Sawant, “Automated aspect extraction and aspect-oriented sentiment analysis on hotel review datasets,” in Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Mumbai, India, Aug. 2018, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697364
O. T. Tran and V. T. Bui, “A BERT-based hierarchical model for Vietnamese aspect-based sentiment analysis,” in Proceedings of the 2020 12th International Conference on Knowledge and Systems Engineering (KSE), Can Tho, Vietnam, Dec. 2020, pp. 269–274. https://doi.org/10.1109/KSE50997.2020.9287650
V. Lanin and E. Smirnova, “Gender variation in the discourse of online hotel reviews: A corpus-based semantic analysis,” in Proceedings of the 14th International Conference on Application of Information and Communication Technologies (AICT), Tashkent, Uzbekistan, Oct. 2020, pp. 1–4. https://doi.org/10.1109/AICT50176.2020.9368872
P. Liu, L. Zhang, and J. A. Gulla, “Multilingual review-aware deep recommender system via aspect-based sentiment analysis,” ACM Transactions on Information Systems, vol. 39, no. 2, Jan. 2021, Art. no. 15. https://doi.org/10.1145/3432049
H. Samy, A. Helmy, and N. Ramadan, “Aspect-based sentiment analysis of mobile apps reviews using class association rules and LDA,” in Proceedings of the Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, Dec. 2021, pp. 183–189. https://doi.org/10.1109/ICICIS52592.2021.9694242
H. S. Lakmisagar and S. M. Sumathi, “Design and implementation of aspect-based sentiment analysis of hotel reviews,” in Proceedings of the 2021 IEEE 6th International Conference on Recent Trends in Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, Aug. 2021. https://doi.org/10.1109/RTEICT52294.2021.9573897
A. N. Azhar and M. L. Khodra, “Fine-tuning pretrained multilingual BERT model for Indonesian aspect-based sentiment analysis,” in Proceedings of the International Conference on Advances in Informatics: Concepts, Theory, and Applications (ICAICTA), Tokoname, Japan, Sep. 2020. https://doi.org/10.1109/ICAICTA49861.2020.9428882
B. Ray, A. Garain, and R. Sarkar, “An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews,” Applied Soft Computing, vol. 98, Jan. 2021, Art. no. 106935. https://doi.org/10.1016/j.asoc.2020.106935
M. D. Pratama, R. Sarno, and R. Abdullah, “Sentiment analysis user regarding hotel reviews by aspect based using latent Dirichlet allocation, semantic similarity, and support vector machine method,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 3, pp. 514–523, 2022.
R. Jayanto, R. Kusumaningrum, and A. Wibowo, “Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory,” International Journal of Advances in Intelligent Informatics, vol. 8, no. 3, pp. 391–403, 2022. https://doi.org/10.26555/ijain.v8i3.691
C.-V. Priporas, A. Vassilikopoulou, and I. Kamenidou, “Negative Airbnb reviews: An aspect-based sentiment analysis approach,” International Journal of Hospitality Management, vol. 91, 2022, Art. no. 102705. https://doi.org/10.1108/EMJB-03-2022-0052
A. Ozcan, B. Emiral, and A. B. Cetin, “Deep hotel recommender system using aspect-based sentiment analysis of users’ reviews,” in Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), Montréal, Canada, Aug. 2022, pp. 3090–3096. https://doi.org/10.1109/ICPR56361.2022.9956570
S. Ghosal and A. Jain, “Weighted aspect-based sentiment analysis using extended OWA operators and Word2Vec for tourism,” Multimedia Tools and Applications, vol. 82, pp. 18353–18380, Oct. 2023. https://doi.org/10.1007/s11042-022-13800-4
M. Kuppusamy and A. Selvaraj, “A novel hybrid deep learning model for aspect-based sentiment analysis,” Concurrency Computation: Practice and Experience, vol. 35, no. 4, Feb. 2023, Art. no. e7538. https://doi.org/10.1002/cpe.7538
N. N. Hidayati, “Improving aspect-based sentiment analysis for hotel reviews with Latent Dirichlet Allocation and machine learning algorithms,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 9, no. 2, pp. 144–159, Jul. 2023. https://doi.org/10.26594/register.v9i2.3441
C. Andreou, N. Tsapatsoulis, and V. Anastasopoulou, “A dataset of hotel reviews for aspect-based sentiment analysis and topic modeling,” in Proceedings of the 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP), Limassol, Cyprus, Sep. 2023, pp. 1–9. https://doi.org/10.1109/SMAP59435.2023.10255219
K. Chhutlani, V. Takrani, A. Motwani, T. Harchandani, and S. Sahu, “Sentiment analysis of OYO hotel reviews using NLP,” in Proceedings of the 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, Jul. 2023, pp. 1–6. https://doi.org/10.1109/ICCCNT56998.2023.10307941
C. S. R. Priya and P. Deepalakshmi, “Sentiment analysis from unstructured hotel reviews data in social network using deep learning techniques,” International Journal of Information Technology, vol. 15, no. 7, pp. 3563–3574, 2023. https://doi.org/10.1007/s41870-023-01419-z
O. G. Horsa and K. K. Tune, “Aspect-based sentiment analysis for Afaan Oromoo movie reviews using machine learning techniques,” Applied Computational Intelligence and Soft Computing, vol. 2023, Dec. 2023, Art. no. 3462691. https://doi.org/10.1155/2023/3462691
P. F. Simmering and P. Huoviala, “Large language models for aspectbased sentiment analysis,” arXiv preprint arXiv:2310.18025, Oct. 2023. https://doi.org/10.48550/arXiv.2310.18025
N. M. S. Iswari, N. Afriliana, E. M. Dharma, and N. P. W. Yuniari, “Enhancing aspect-based sentiment analysis in visitor review using semantic similarity,” Journal of Applied Data Sciences, vol. 5, no. 2, pp. 724–735, 2024. https://doi.org/10.47738/jads.v5i2.249
I. Nawawi, K. F. Ilmawan, M. R. Maarif, and M. Syafrudin, “Exploring tourist experience through online reviews using aspect-based sentiment analysis with zero-shot learning for hospitality service enhancement,” Information, vol. 15, no. 8, Aug. 2024, Art. no. 499. https://doi.org/10.3390/info15080499
X. Jiang, P. You, C. Chen, Z. Wang, and G. Zhou, “Exploring scope detection for aspect-based sentiment analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 83–92, Oct. 2024. https://doi.org/10.1109/TASLP.2023.3323136
N. Mughal, G. Mujtaba, S. Shaikh, A. Kumar, and S. M. Daudpota, “Comparative analysis of deep neural networks and large language models for aspect-based sentiment analysis,” IEEE Access, vol. 12, pp. 60943–60956, 2024. https://doi.org/10.1109/ACCESS.2024.3386969
J. Yang, M. Xu, Y. Xiao, and X. Du, “AMIFN: Aspect-guided multi-view interactions and fusion network for multimodal aspect-based sentiment analysis,” Neurocomputing, vol. 573, Mar. 2024, Art. no. 127222. https://doi.org/10.1016/j.neucom.2023.127222
J. Fu, X. Li, Y. Zhu, Y. Du, Y. Fan, X. Chen, D. Huang, and S. Wang, “An implicit aspect-based sentiment analysis method using supervised contrastive learning and knowledge embedding,” Applied Soft Computing, vol. 167, Dec. 2024, Art. no. 112233. https://doi.org/10.1016/j.asoc.2024.112233
K. Subbaiah, B. K. Bolla, “Aspect category learning and sentimental analysis using weakly supervised learning,” Procedia Computer Science, vol. 235, pp. 1246–1257, 2024. https://doi.org/10.1016/j.procs.2024.04.118
L. Yang, J.-C. Na, and J. Yu, “Cross-modal multitask transformer for end-to-end multimodal aspect-based sentiment analysis,” Information Processing and Management, vol. 59, no. 5, Sep. 2024. Art. no. 103038. https://doi.org/10.1016/j.ipm.2022.103038
T. T. Doan, T. Q. Tran, D. T. Le, A. H. Tran, A. T. Nguyen, T. H. A. Le, T. N. T. Doan, S. T. Huynh, and B. T. Nguyen, “HOSSemEval-EB23: A robust dataset for aspect-based sentiment analysis of hospitality reviews,” Multimedia Tools and Applications, vol. 83, pp. 81279–81297, Jun. 2024. https://doi.org/10.1007/s11042-024-19518-9
L.-C. Cheng, H.-Y. Huang, and Y.-W. Huang, “Multi-task Chinese aspect-based sentiment analysis framework for service improvement: A case study on BNB reviews,” Electronic Commerce Research, vol. 1, pp. 1–10, 2024. https://doi.org/10.1007/s10660-024-09871-0
H. Wang, X. Qiu, and X. Tan, “Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis,” Applied Intelligence, vol. 54, pp. 11672–11689, Aug. 2024. https://doi.org/10.1007/s10489-024-05802-6
J. Chen and L. Pan, “An AI-based cross-language aspect-level sentiment analysis model using English corpus,” Engineering Reports, vol. 6, no. 12, Jul. 2024, Art. no. e12969. https://doi.org/10.1002/eng2.12969
P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, and T. Kumaravel, “Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review,” Computational Intelligence, vol. 40, no. 5, Oct. 2024, Art. no. e12698. https://doi.org/10.1111/coin.12698
C. Zhu and Q. Ding, “Aspect-based sentiment analysis via dual residual networks with sentiment knowledge,” The Journal of Supercomputing, vol. 81, 2024, Art. no. 131. https://doi.org/10.1007/s11227-024-06546-3
B. Zeng, L. Xie, R. Li, Y. Yao, R. Li, and H. Deng, “Aspect-aware semantic feature enhanced networks for multimodal aspect-based sentiment analysis,” The Journal of Supercomputing, vol. 81, Oct. 2025, Art. no. 64. https://doi.org/10.1007/s11227-024-06472-4
X. Jiang, B. Ren, Q. Wu, W. Wang, and H. Li, “DCASAM: Advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model,” Complex & Intelligent Systems, vol. 10, pp. 7907–7926, Aug. 2024. https://doi.org/10.1007/s40747-024-01570-5
K. Aziz, D. Ji, P. Chakrabarti, T. Chakrabarti, M. S. Iqbal, and R. Abbasi, “Unifying aspect-based sentiment analysis BERT and multi-layered graph convolutional networks for comprehensive sentiment dissection,” Scientific Reports, vol. 14, Jun. 2024, Art. no. 14646. https://doi.org/10.1038/s41598-024-61886-7
R. Wadawadagi, S. Tiwari, and V. Pagi, “Polarity-aware deep attention network for aspect-based sentiment analysis,” Progress in Artificial Intelligence, vol. 33, no. 1, 2024, Art. no. 2350054. https://doi.org/10.1142/S0218213023500549
G. Dubey, K. Kaur, A. Chadha, G. Raj, S. Jain, and A. K. Dubey, “A domain knowledge infused gated network using integrated sentiment prediction framework for aspect-based sentiment analysis,” Evolving Systems, vol. 16, no. 12, 2025, Art. no. 12. https://doi.org/10.1007/s12530-024-09625-1
Y. Du, R. Xie, B. Zhang, and Z. Yin, “FMCF: Few-shot multimodal aspect-based sentiment analysis framework based on contrastive finetuning,” Applied Intelligence, vol. 54, pp. 12629–12643, Sep. 2024. https://doi.org/10.1007/s10489-024-05841-z
X. Huang, H. Sun, Q. Gao, W. Huang, and R. Cao, “Utilizing large language models for event deconstruction to enhance multimodal aspectbased sentiment analysis,” arXiv preprint arXiv:2407.04150, Oct. 2024. https://doi.org/10.48550/arXiv.2410.14150
S. Song, “Exploring large language models for multimodal sentiment analysis: Challenges, benchmarks, and future directions,” in Proceedings of the International Conference on Multimodal Sentiment Analysis, 2024.
N. Alturayeif and I. Ahmad, “EASE: An enhanced active learning framework for aspect-based sentiment analysis based on sample diversity and data augmentation,” Expert Systems with Applications, vol. 261, Feb. 2025, Art. no. 125525. https://doi.org/10.1016/j.eswa.2024.125525