Abstract
The rise of e-commerce due to the Covid-19 situation is becoming more significant in 2021. It could lead to great demands to understand customers’ opinions usually shown in their reviews. An e-commerce platform with the ability to be aware of its users’ viewpoint can have a higher possibility of meeting customer expectations, attracting new users, and increasing sales. With the tremendous data in e-commerce platforms presently, sentiment analysis is a powerful tool to understand users. However, the sentiment in reviews data may contain more than two states, positive and negative, and then a binary sentiment classifier may not be helpful in practice. According to our knowledge, research on this subject is often restricted access. Therefore, this paper presents a multi-class sentiment analysis for Vietnamese reviews on a large-scale dataset, including 480,702 reviews. We collected these reviews from popular Vietnamese e-commerce websites and manually did the labeling process with three classes of sentiments (positive, negative, and neutral). To build a suitable classification model for the main problem, we propose a deep learning approach using different architectures (LSMT, GRU, TextCNN, LSTM + CNN, and GRU+CNN) and compare the performance among other ensemble techniques. The experimental results show the outperformance of the ensemble techniques on the multi-class sentiment classification problem, and the combination of chosen architectures using the attention mechanism could obtain the best F-1 score of \(73.64\%\).
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References
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014)
Contratres, F.G., Alves-Souza, S.N., Filgueiras, L.V.L., DeSouza, L.S.: Sentiment analysis of social network data for cold-start relief in recommender systems. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST’18 2018. AISC, vol. 746, pp. 122–132. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77712-2_12
Devlin, J., Chang, M.-W., Lee, K.: Bert: pretraining of deep bidirectional transformers for language understanding (2019)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar (2014)
Lai, S., Liheng, X., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Bonet, B., Koenig, S. (eds.) AAAI, vol. 333, pp. 2267–2273 (2015)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13, Red Hook, NY, USA, vol.2, pp. 3111–3119. Curran Associates Inc (2013)
Nguyen, C.V., Le, K.H., Nguyen, B.T.: A novel approach for enhancing vietnamese sentiment classification. In: Fujita, H., Selamat, A., Lin, J.C.-W., Ali, M. (eds.) IEA/AIE 2021. LNCS (LNAI), vol. 12799, pp. 99–111. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79463-7_9
Nguyen, V.C., Le, K.H., Nguyen, B.T.: An efficient framework for Vietnamese sentiment analysis. In: Proceedings of The 18th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT) (2020)
Nguyen, H., et al.: Multi-level sentiment analysis of product reviews based on grammar rules. In: New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT_21), vol. 337, pp. 444–456. IOS Press (2021)
Pennington, E., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)s, pp. 1532–1543 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computing 9, 1753–1780 (1997)
Vo, H., Lam, H., Nguyen, D.D., Tuong, N.: Topic classification and sentiment analysis for Vietnamese education survey system. Asian J. Comput. Sci. Inf. Technol. 6, 27–34 (2016)
Wang, G., Sun, J., Ma, J., Kaiquan, X., Jibao, G.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. 57, 77–93 (2014)
Wilcox, E., Levy, R., Morita, T., Futrell, R.: What do RNN language models learn about filler-gap dependencies?. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 211–221 (2018)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 8(4), e1253 (2018)
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We want to thank the University of Science, Vietnam National University in Ho Chi Minh City, and AISIA Research Lab in Vietnam for supporting us throughout this paper.
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Nguyen, C.T.V., Tran, A.M., Nguyen, T., Nguyen, T.T., Nguyen, B.T. (2022). Multi-class Sentiment Classification for Customers’ Reviews. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_49
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