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SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding

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Distributed Computing and Intelligent Technology (ICDCIT 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14501))

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Abstract

In recent years, sentiment analysis has grown more intricate as the need for deeper insights from text data has expanded. Traditional methods fall short for capturing subtle opinions, giving rise to aspect-oriented sentiment analysis. This study proposes a new framework called Sentiment Analysis with Aspect-Specific Evaluation (SASE) fusing with diverse word embeddings to give aspect-specific sentiment analysis. This novel hybrid approach holds the promise of unravelling multifaceted sentiment aspects across varied domains, and when coupled with the robust RoBERTa model, demonstrates good improvements in accuracy with 78%. The comparison study of the SASE framework with baseline models are also discussed in this work.

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References

  1. Singh, M., Jakhar, A.K., Pandey, S.: Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc. Netw. Anal. Min. 11(1), 1–11 (2021)

    Article  Google Scholar 

  2. Mewada, A., Dewang, R.K.: SA-ASBA: a hybrid model for aspect-based sentiment analysis using synthetic attention in pre-trained language BERT model with extreme gradient boosting. J. Supercomput. 79(5), 5516–5551 (2023)

    Article  Google Scholar 

  3. Kathuria, A., Gupta, A., Singla, R.: AOH-Senti: aspect-oriented hybrid approach to sentiment analysis of students’ feedback. SN Comput. Sci. 4(2), 152 (2023)

    Article  Google Scholar 

  4. Feng, J., Cai, S., Ma, X.: Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm. Clust. Comput. 22, 5839–5857 (2019)

    Article  Google Scholar 

  5. Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, C., Gurevych, I.: Classification and clustering of arguments with contextualized word embeddings. arXiv preprint arXiv:1906.09821 (2019)

  6. Thet, T.T., Na, J.-C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)

    Article  Google Scholar 

  7. Truşcǎ, M.M., Wassenberg, D., Frasincar, F., Dekker, R.: A hybrid approach for aspect-based sentiment analysis using deep contextual word embeddings and hierarchical attention. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds.) ICWE 2020. LNCS, vol. 12128, pp. 365–380. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50578-3_25

    Chapter  Google Scholar 

  8. Meškelė, D., Frasincar, F.: ALDONAr: a hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalized domain ontology and a regularized neural attention model. Inf. Process. Manage. 57(3), 102211 (2020)

    Article  Google Scholar 

  9. Pham, D.-H., Le, A.-C.: Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis. Int. J. Approximate Reasoning 103, 1–10 (2018)

    Article  MathSciNet  Google Scholar 

  10. Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086 (2018)

  11. Wang, S., Mazumder, S., Liu, B., Zhou, M., Chang, Y.: Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2018)

    Google Scholar 

  12. Qi, Y., Zheng, X., Huang, X.: Aspect-based sentiment analysis with enhanced aspect-sensitive word embeddings. Knowl. Inf. Syst. 64(7), 1845–1861 (2022)

    Article  Google Scholar 

  13. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation (2014)

    Google Scholar 

  14. Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015)

    Google Scholar 

  15. Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: 10th International Workshop on Semantic Evaluation (SemEval 2016) (2016)

    Google Scholar 

  16. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)

    Google Scholar 

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

  20. Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)

  21. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  22. Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

  23. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016)

  24. Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 197–206. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93372-6_22

    Chapter  Google Scholar 

  25. Tay, Y., Tuan, L.A., Hui, S.C.: Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  26. Huang, B., Carley, K.M.: Parameterized convolutional neural networks for aspect level sentiment classification. arXiv preprint arXiv:1909.06276 (2019)

  27. Zhu, P., Qian, T.: Enhanced aspect level sentiment classification with auxiliary memory. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1077–1087 (2018)

    Google Scholar 

  28. Nguyen, H.T., Le Nguyen, M.: Effective attention networks for aspect-level sentiment classification. In: 2018 10th International Conference on Knowledge and Systems Engineering (KSE), pp. 25–30. IEEE (2018)

    Google Scholar 

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Correspondence to Annushree Bablani .

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TK, B., Bablani, A., SR, S., Misra, H. (2024). SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding. In: Devismes, S., Mandal, P.S., Saradhi, V.V., Prasad, B., Molla, A.R., Sharma, G. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2024. Lecture Notes in Computer Science, vol 14501. Springer, Cham. https://doi.org/10.1007/978-3-031-50583-6_16

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  • DOI: https://doi.org/10.1007/978-3-031-50583-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50582-9

  • Online ISBN: 978-3-031-50583-6

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