Abstract
With technological advancements, the proliferation of e-commerce websites and social media platforms has created an avenue for customers to provide feedback to enterprises based on their overall experience. Customer feedback serves as an independent validation tool that could boost consumer trust in the brand. Whether it is a recommendation or review of a product, it provides insight allowing businesses to understand what they are doing right or wrong. By automatically analyzing customer complaints at the aspect-level enterprises can connect to their customers by customizing products and services according to their needs quickly and deftly. In this paper, we introduce the task of Aspect-Based Complaint Detection (ABCD). ABCD identifies the aspects in the given review about a product and also finds if the aspect mentioned in the review signifies a complaint or non-complaint. Specifically, a task solver must detect duplets (What, How) from the inputs that show WHAT the targeted features are and HOW they are complaints. To address this challenge, we propose a deep-learning-based multi-modal framework, where the first stage predicts what the targeted aspects are, and the second stage categorizes whether the targeted aspect is associated with a complaint or not. We annotate the aspect categories and associated complaint/non-complaint labels in the recently released multi-modal complaint dataset (CESAMARD), which spans five domains (books, electronics, edibles, fashion, and miscellaneous). Based on extensive evaluation our methodology established a benchmark performance in this novel aspect-based complaint detection task and also surpasses a few strong baselines developed from state-of-the-art related methods (Resources available at: https://github.com/appy1608/ECIR2023_Complaint-Detection).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
The foreground items are enclosed in rectangular bounding boxes.
- 5.
- 6.
- 7.
- 8.
- 9.
Kindly note we do not report the results for miscellaneous domain as it consists of 40 instances, which is insufficient for training a deep learning model.
- 10.
The results are found to be statistically significant when testing the null hypothesis (p-value < 0.05).
References
Akhtar, M.S., Ekbal, A., Bhattacharyya, P.: Aspect based sentiment analysis: category detection and sentiment classification for hindi. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 246–257. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75487-1_19
Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555–596 (2008)
Bhat, S., Culotta, A.: Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11 (2017)
Cai, Y., Cai, H., Wan, X.: Multi-modal sarcasm detection in twitter with hierarchical fusion model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2506–2515 (2019)
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103–111. Association for Computational Linguistics (2014). https://doi.org/10.3115/v1/W14-4012, https://www.aclweb.org/anthology/W14-4012/
Coussement, K., Van den Poel, D.: Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decis. Supp. Syst. 44(4), 870–882 (2008)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. 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, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423
Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378 (1971)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jin, M., Aletras, N.: Complaint identification in social media with transformer networks. In: Scott, D., Bel, N., Zong, C. (eds.) Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), 8–13 December 2020, pp. 1765–1771. International Committee on Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.coling-main.157, https://doi.org/10.18653/v1/2020.coling-main.157
Jin, M., Aletras, N.: Modeling the severity of complaints in social media. In: Toutanova, K., et al. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, 6–11 June 2021, pp. 2264–2274. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.naacl-main.180
Kiela, D., Bhooshan, S., Firooz, H., Perez, E., Testuggine, D.: Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lailiyah, M., Sumpeno, S., Purnama, I.E.: Sentiment analysis of public complaints using lexical resources between Indonesian sentiment lexicon and sentiwordnet. In: 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 307–312. IEEE (2017)
Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on roberta. Appl. Intell. 51(6), 3522–3533 (2021)
Liu, Y., Liu, H., Wong, L.-P., Lee, L.-K., Zhang, H., Hao, T.: A hybrid neural network RBERT-C based on pre-trained RoBERTa and CNN for user intent classification. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2020. CCIS, vol. 1265, pp. 306–319. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-7670-6_26
Liu, Z., Lin, W., Shi, Y., Zhao, J.: A robustly optimized BERT pre-training approach with post-training. In: Li, S., et al. (eds.) CCL 2021. LNCS (LNAI), vol. 12869, pp. 471–484. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84186-7_31
Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Adv. Neural Inf. Process. Syst. 32, 1–11 (2019)
Olshtain, E., Weinbach, L.: Complaints: a study of speech act behavior among native and nonnative speakers of hebrew. the prag-matic perspective (1985)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: Meld: a multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508 (2018)
Preotiuc-Pietro, D., Gaman, M., Aletras, N.: Automatically identifying complaints in social media. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July–2 August 2019, vol. 1: Long Papers, pp. 5008–5019. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1495
Saha, T., Patra, A.P., Saha, S., Bhattacharyya, P.: Towards emotion-aided multi-modal dialogue act classification. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, 5–10 July 2020, pp. 4361–4372. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.402
Saha, T., Upadhyaya, A., Saha, S., Bhattacharyya, P.: Towards sentiment and emotion aided multi-modal speech act classification in twitter. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5727–5737 (2021)
Singh, A., Dey, S., Singha, A., Saha, S.: Sentiment and emotion-aware multi-modal complaint identification. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, 22 February–1 March 2022, pp. 12163–12171. AAAI Press (2022). https://ojs.aaai.org/index.php/AAAI/article/view/21476
Singh, A., Nazir, A., Saha, S.: Adversarial multi-task model for emotion, sentiment, and sarcasm aided complaint detection. In: Hagen, M., Verberne, S., Macdonald, C., Seifert, C., Balog, K., Nørvåg, K., Setty, V. (eds.) ECIR 2022. LNCS, vol. 13185, pp. 428–442. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99736-6_29
Singh, A., Saha, S.: Are you really complaining? a multi-task framework for complaint identification, emotion, and sentiment classification. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 715–731. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86331-9_46
Singh, A., Saha, S., Hasanuzzaman, M., Dey, K.: Multitask learning for complaint identification and sentiment analysis. Cogn. Comput., 1–16 (2021)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Trosborg, A.: Interlanguage Pragmatics: Requests, Complaints, and Apologies, vol. 7. Walter de Gruyter (2011)
Vásquez, C.: Complaints online: the case of tripadvisor. J. Pragmat. 43(6), 1707–1717 (2011)
Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017). http://arxiv.org/abs/1706.03762
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Welch, B.L.: The generalization of ‘student’s’problem when several different population varlances are involved. Biometrika 34(1–2), 28–35 (1947)
Yang, W., et al.: Detecting customer complaint escalation with recurrent neural networks and manually-engineered features. In: Loukina, A., Morales, M., Kumar, R. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, vol. 2 (Industry Papers), pp. 56–63. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-2008
Acknowledgement
This publication is an outcome of the R &D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (Formerly Media Lab Asia).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, A., Gangwar, V., Sharma, S., Saha, S. (2023). Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-28238-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28237-9
Online ISBN: 978-3-031-28238-6
eBook Packages: Computer ScienceComputer Science (R0)