Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning
DOI:
https://doi.org/10.53469/jtpes.2024.04(04).01Keywords:
Deep learning, Sentiment Analysis, Aspect Based Sentiment Analysis, Machine learningAbstract
In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.
References
Zhang W, Li X, Deng Y, et al. A survey on aspect-based sentiment analysis: Tasks, methods, and challenges[J]. IEEE Transactions on Knowledge and Data Engineering, 2022.
Brauwers G, Frasincar F. A survey on aspect-based sentiment classification[J]. ACM Computing Surveys, 2022, 55(4): 1-37.
Nazir A, Rao Y, Wu L, et al. Issues and challenges of aspect-based sentiment analysis: A comprehensive survey[J]. IEEE Transactions on Affective Computing, 2020, 13(2): 845-863.
Liu H, Chatterjee I, Zhou M C, et al. Aspect-based sentiment analysis: A survey of deep learning methods[J]. IEEE Transactions on Computational Social Systems, 2020, 7(6): 1358-1375.
Soni P K, Rambola R. A survey on implicit aspect detection for sentiment analysis: terminology, issues, and scope[J]. IEEE Access, 2022, 10: 63932-63957.
Sabeeh A, Dewang R K. Comparison, classification and survey of aspect based sentiment analysis[C]//Advanced Informatics for Computing Research: Second International Conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I 2. Springer Singapore, 2019: 612-629.
Do H H, Prasad P W C, Maag A, et al. Deep learning for aspect-based sentiment analysis: a comparative review[J]. Expert systems with applications, 2019, 118: 272-299.
Laskari N K, Sanampudi S K. Aspect based sentiment analysis survey[J]. IOSR Journal of Computer Engineering (IOSR-JCE), 2016, 18(2): 24-28.
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.
Li Z, Liu F, Yang W, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE transactions on neural networks and learning systems, 2021, 33(12): 6999-7019.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
Cai H, Tu Y, Zhou X, et al. Aspect-category based sentiment analysis with hierarchical graph convolutional network[C]//Proceedings of the 28th international conference on computational linguistics. 2020: 833-843.
Zhang Y, Wallace B. A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1510.03820, 2015.
Wang F, Lan M, Wang W. Towards a one-stop solution to both aspect extraction and sentiment analysis tasks with neural multi-task learning[C]//2018 International joint conference on neural networks (IJCNN). IEEE, 2018: 1-8.
Li X, Bing L, Li P, et al. A unified model for opinion target extraction and target sentiment prediction[C]//Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 6714-6721.
He R, Lee W S, Ng H T, et al. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis[J]. arXiv preprint arXiv:1906.06906, 2019.
Schmitt M, Steinheber S, Schreiber K, et al. Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks[J]. arXiv preprint arXiv:1808.09238, 2018.
Chen Y. Convolutional neural network for sentence classification[D]. University of Waterloo, 2015.
Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv:1412.3555, 2014.
Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.
Kirange D, Deshmukh R R, Kirange M. Aspect based sentiment analysis semeval-2014 task 4[J]. Asian Journal of Computer Science and Information Technology (AJCSIT) Vol, 2014, 4.
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015. SemEval-2015 Task 12: Aspect Based Sentiment Analysis. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 486–495, Denver, Colorado. Association for Computational Linguistics.
Pontiki M, Galanis D, Papageorgiou H, et al. Semeval-2016 task 5: Aspect based sentiment analysis[C]//ProWorkshop on Semantic Evaluation (SemEval-2016). Association for Computational Linguistics, 2016: 19-30.
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[C]//Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2011: 315-323.
Xu, J., Wu, B., Huang, J., Gong, Y., Zhang, Y., & Liu, B. (2024). Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis. arXiv preprint arXiv:2403.17549.
Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management. arXiv preprint arXiv:2402.17216.
Gong, Y., Huang, J., Liu, B., Xu, J., Wu, B., & Zhang, Y. (2024). Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning. arXiv preprint arXiv:2403.13619.
Liu, B. (2023). Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning. International Journal of Computer Science and Information Technology, 1(1), 17-23.
Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent Robotic Control System Based on Computer Vision Technology. arXiv preprint arXiv:2404.01116.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Yadong Shi, Lianwei Li, Huixiang Li, Ang Li, Yiyu Lin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
