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Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement

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Abstract

Most implicit sentiment sentences in aspect level implicit sentiment analysis lack emotional words, but there are still potential emotional clues. Current research mainly utilizes contextual information and external knowledge of text for semantic enhancement, thereby improving implicit sentiment analysis. However, in these models, the semantic correlation between words decays as the distance between the two increases, leading to weak and difficult to capture emotional cues at long distances, presenting ambiguity in emotional polarity and reducing the performance of sentiment analysis. To solve this problem, in this paper, we propose aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. Firstly, the semantic wave-based aspect word context information enhancement method uses the fluctuation property of semantic wave to perform weight adaptive enhancement of semantically related long-distance context information. This method essentially utilizes a sine wave function to significantly reduce the weight of words that are close but semantically unrelated, thereby allowing the weight of words that are close and semantically similar to be relatively reflected, in order to solve the problem of hiding emotional clues at long distances. Then, the implicit sentiment analysis method based on supervised contrastive pre-training is used to capture potential sentiment cues from context information and embed them into the sentence vector by weak feature learning of implicit sentiment. Finally, multi-feature interaction-based knowledge enhancement method is used to focus on the context content of sentences that are more related to background knowledge in a feature interaction way, and the information scaling factor is combined to enhance the dependency between aspect words and sentiment cues, thereby solving the problem of weak features in long-distance emotional clues and accurately identifying the emotional polarity of aspect words. The experiment shows that the model proposed in this paper has made significant improvements on the Restaurant and Laptop datasets in SemEval 2014 Task 4, with an accuracy increase of 3.38% and 4.57% in implicit emotions, respectively, achieving the best performance so far. At the same time, generalization and robustness testing experiments were conducted on the model, achieving preferable performance improvement.

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Data availability

All datasets are commonly used in NLP. The corresponding links are as follows: https://semeval.github.io/SemEval2021/tasks.

Code availability

If the paper is accepted, we are pleased to share the code.

References

  1. Xu M, Wang D, Feng S, Yang Z, Zhang Y (2022) KC-ISA: an implicit sentiment analysis model combining knowledge enhancement and context features. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 6906–6915

  2. Schouten K, Frasincar F (2015) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830

    Article  Google Scholar 

  3. Devi Sri Nandhini M, Pradeep G (2020) A hybrid co-occurrence and ranking-based approach for detection of implicit aspects in aspect-based sentiment analysis. SN Comput Sci 1:1–9

    Article  Google Scholar 

  4. Zhuang Y, Liu Z, Liu T-T, Hung C-C, Chai Y-J (2022) Implicit sentiment analysis based on multi-feature neural network model. Soft Comput 26(2):635–644

    Article  Google Scholar 

  5. Wang S, Zhou J, Sun C, Ye J, Gui T, Zhang Q, Huang X (2022) Causal intervention improves implicit sentiment analysis. In: Calzolari N, Huang C-R, Kim H, Pustejovsky J, Wanner L, Choi K-S, Ryu P-M, Chen H-H, Donatelli L, Ji H, Kurohashi S, Paggio P, Xue N, Kim S, Hahm Y, He Z, Lee TK, Santus E, Bond F, Na S-H (eds.) Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Gyeongju, pp 6966–6977

  6. Hajar EH, Mohammed B (2019) Using synonym and definition wordnet semantic relations for implicit aspect identification in sentiment analysis. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security, pp 1–5

  7. Cao Z, Wang S, Wang H, Zhang W (2022) Implicit sentiment analysis of Chinese texts based on contextual information and knowledge enhancement. IEEE Data Eng Bull 45(4):72–87

    Google Scholar 

  8. Benarafa H, Benkhalifa M, Akhloufi M (2023) WordNet semantic relations based enhancement of KNN model for implicit aspect identification in sentiment analysis. Int J Comput Intell Syst 16(1):3

    Article  Google Scholar 

  9. Wei J, Liao J, Yang Z, Wang S, Zhao Q (2020) BiLSTM with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing 383:165–173

    Article  Google Scholar 

  10. Zuo E, Zhao H, Chen B, Chen Q (2020) Context-specific heterogeneous graph convolutional network for implicit sentiment analysis. IEEE Access 8:37967–37975

    Article  Google Scholar 

  11. Qian Y, Wang J, Li D, Zhang X (2023) Interactive capsule network for implicit sentiment analysis. Appl Intell 53(3):3109–3123

    Article  Google Scholar 

  12. Li Z, Zou Y, Zhang C, Zhang Q, Wei Z (2021) Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Moens M-F, Huang X, Specia L, Yih SW-T (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp. 246–256. https://doi.org/10.18653/v1/2021.emnlp-main.22 . https://aclanthology.org/2021.emnlp-main.22

  13. Zhao Y, Mamat M, Aysa A, Ubul K (2023) Knowledge-fusion-based iterative graph structure learning framework for implicit sentiment identification. Sensors 23(14):6257

    Article  Google Scholar 

  14. Gu T, Zhao H, He Z, Li M, Ying D (2023) Integrating external knowledge into aspect-based sentiment analysis using graph neural network. Knowl Based Syst 259:110025. https://doi.org/10.1016/j.knosys.2022.110025

    Article  Google Scholar 

  15. Ma H, Guo H (2023) A hybrid model based on multi-level external knowledge for Chinese semantic matching. In: 2023 IEEE International Conference on Big Data (BigData), pp 1200–1205. https://doi.org/10.1109/BigData59044.2023.10386404

  16. Lai TM, Castellucci G, Kuzi S, Ji H, Rokhlenko O (2023) External knowledge acquisition for end-to-end document-oriented dialog systems. In: Vlachos A, Augenstein I (eds.) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Dubrovnik, Croatia, pp 3633–3647. https://doi.org/10.18653/v1/2023.eacl-main.264. https://aclanthology.org/2023.eacl-main.264

  17. Liu C, Li X, Shang L, Jiang X, Liu Q, Lam E, Wong N (2023) Gradually excavating external knowledge for implicit complex question answering. In: Bouamor H, Pino J, Bali K (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics, Singapore, pp 14405–14417. https://doi.org/10.18653/v1/2023.findings-emnlp.961. https://aclanthology.org/2023.findings-emnlp.961

  18. Nie Y, Zhang Y, Peng Y, Yang L (2022) Borrowing wisdom from world: modeling rich external knowledge for Chinese named entity recognition. Neural Comput Appl 34(6):4905–4922

    Article  Google Scholar 

  19. Wang Z, Li L, Zeng D (2020) Knowledge-enhanced natural language inference based on knowledge graphs. In: Scott D, Bel N, Zong C (eds.) Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain, pp 6498–6508. https://doi.org/10.18653/v1/2020.coling-main.571. https://aclanthology.org/2020.coling-main.571

  20. Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019) ERNIE: enhanced language representation with informative entities. In: Korhonen A, Traum D, Màrquez L (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, pp 1441–1451. https://doi.org/10.18653/v1/P19-1139. https://aclanthology.org/P19-1139

  21. Teo A, Wang, Z, Pen H, Subagdja B, Ho S-B, Quek BK (2023) Knowledge graph enhanced aspect-based sentiment analysis incorporating external knowledge. In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp 791–798. https://doi.org/10.1109/ICDMW60847.2023.00107

  22. Hu Z, Wang Y (2021) Utilizing external knowledge with multi-granularity attention for review reading comprehension. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp 1231–1236. https://doi.org/10.1109/ICTAI52525.2021.00195

  23. He R, Lee WS, Ng HT, Dahlmeier D (2018) Effective attention modeling for aspect-level sentiment classification. In: Bender EM, Derczynski L, Isabelle P (eds.) Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, pp 1121–1131. https://aclanthology.org/C18-1096

  24. Kim J, El-Khamy M, Lee J (2020) T-GSA: transformer with gaussian-weighted self-attention for speech enhancement. In: ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 6649–6653. https://doi.org/10.1109/ICASSP40776.2020.9053591

  25. Guo M, Zhang Y, Liu T (2019) Gaussian transformer: a lightweight approach for natural language inference. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 6489–6496

  26. Wei X (2010) Sine-wave-based text watermark for word document. In: 2010 International Conference on Computer and Information Application, pp 99–102

  27. Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20. Curran Associates Inc., Red Hook

  28. Liu W, Zhou P, Zhao Z, Wang Z, Ju Q, Deng H, Wang P (2020) K-BERT: enabling language representation with knowledge graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 2901–2908

  29. Sun T, Shao Y, Qiu X, Guo Q, Hu Y, Huang X, Zhang Z (2020) CoLAKE: Contextualized language and knowledge embedding. In: Scott D, Bel N, Zong C (eds.) Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain, pp 3660–3670. https://doi.org/10.18653/v1/2020.coling-main.327. https://aclanthology.org/2020.coling-main.327

  30. Wang X, Gao T, Zhu Z, Zhang Z, Liu Z, Li J, Tang J (2021) KEPLER: a unified model for knowledge embedding and pre-trained language representation. Trans Assoc Comput Linguist 9:176–194. https://doi.org/10.1162/tacl_a_00360

    Article  Google Scholar 

  31. Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: aspect based sentiment analysis. In: Nakov P, Zesch T (eds.) Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Association for Computational Linguistics, Dublin, pp 27–35https://doi.org/10.3115/v1/S14-2004. https://aclanthology.org/S14-2004

  32. Jiang Q, Chen L, Xu R, Ao X, Yang M (2019) A challenge dataset and effective models for aspect-based sentiment analysis. In: Inui K, Jiang J, Ng V, Wan X (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, pp 6280–6285. https://doi.org/10.18653/v1/D19-1654. https://aclanthology.org/D19-1654

  33. Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Su J, Duh K, Carreras X (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, pp 606–615. https://doi.org/10.18653/v1/D16-1058. https://aclanthology.org/D16-1058

  34. Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. ar**v preprint ar**v:1709.00893

  35. Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Palmer M, Hwa R, Riedel S (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, pp 452–461. https://doi.org/10.18653/v1/D17-1047. https://aclanthology.org/D17-1047

  36. Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, pp 3433–3442. https://doi.org/10.18653/v1/D18-1380. https://aclanthology.org/D18-1380

  37. Scaria K, Gupta H, Goyal S, Sawant SA, Mishra S, Baral C (2023) InstructABSA: instruction learning for aspect based sentiment analysis. ar**v preprint ar**v:2302.08624

  38. Yang H, Li K (2024) Modeling aspect sentiment coherency via local sentiment aggregation. In: Graham Y, Purver M (eds.) Findings of the Association for Computational Linguistics: EACL 2024. Association for Computational Linguistics, St. Julian’s, pp 182–195. https://aclanthology.org/2024.findings-eacl.13

  39. Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Inui K, Jiang J, Ng V, Wan X (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, pp 4568–4578. https://doi.org/10.18653/v1/D19-1464. https://aclanthology.org/D19-1464

  40. Zhang M, Qian T (2020) Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Webber B, Cohn T, He Y, Liu, Y (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp 3540–3549. https://doi.org/10.18653/v1/2020.emnlp-main.286. https://aclanthology.org/2020.emnlp-main.286

  41. Sun K, Zhang R, Mensah S, Mao Y, Liu X (2019) Aspect-level sentiment analysis via convolution over dependency tree. In: Inui K, Jiang J, Ng V, Wan X (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, pp 5679–5688. https://doi.org/10.18653/v1/D19-1569. https://aclanthology.org/D19-1569

  42. Wang K, Shen W, Yang Y, Quan X, Wang R (2020) Relational graph attention network for aspect-based sentiment analysis. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp 3229–3238. https://doi.org/10.18653/v1/2020.acl-main.295. https://aclanthology.org/2020.acl-main.295

  43. Chen Z, Qian T (2019) Transfer capsule network for aspect level sentiment classification. In: Korhonen A, Traum D, Màrquez L (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, pp 547–556. https://doi.org/10.18653/v1/P19-1052. https://aclanthology.org/P19-1052

  44. Xu H, Liu B, Shu L, Yu P (2019) BERT post-training for review reading comprehension and aspect-based sentiment analysis. In: Burstein J, Doran C, Solorio T (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, pp 2324–2335. https://doi.org/10.18653/v1/N19-1242. https://aclanthology.org/N19-1242

  45. Rietzler A, Stabinger S, Opitz P, Engl S (2020) Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspect-target sentiment classification. In: Calzolari N, Béchet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S (eds.) Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, pp 4933–4941. https://aclanthology.org/2020.lrec-1.607

  46. Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579–2605

    Google Scholar 

  47. **ng X, ** Z, ** D, Wang B, Zhang Q, Huang X (2020) Tasty burgers, soggy fries: probing aspect robustness in aspect-based sentiment analysis. In: Webber B, Cohn T, He Y, Liu Y (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp 3594–3605. https://doi.org/10.18653/v1/2020.emnlp-main.292. https://aclanthology.org/2020.emnlp-main.292

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Funding

This work was supported by Key Research Planning Project of National Language Committee (No. ZDI145-94).

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Author notes

  1. Maoyuan Zhang and Fei Wu have contributed equally to this work.

Authors and Affiliations

  1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang, Fei Wu, WeiLiang Chen & **ang Li

  2. School of Computer Science, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang, Fei Wu, WeiLiang Chen & **ang Li

  3. National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang

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Contributions

All authors contributed to the study conception and design. Code implementation, data collection and analysis were performed by Maoyuan Zhang, Fei Wu, WeiLiang Chen and **ang Li. The first draft of the manuscript was written by Fei Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors contributed equally to this paper.

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Correspondence to WeiLiang Chen.

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Zhang, M., Wu, F., Chen, W. et al. Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06255-x

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