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Sentiment Analysis Method for Douban Movie Reviews Based on Prompt Learning
- 1 Henan University, Kaifeng, China
* Author to whom correspondence should be addressed.
Abstract
Movie reviews reflect the viewers’ evaluations of films. Conducting sentiment analysis on these reviews not only helps viewers select films they enjoy but also provides guidance for filmmakers in their creative processes. The sentiment analysis task for movie reviews has evolved from sentiment lexicon construction and machine learning to deep learning. These methods all rely on the fine-tuning paradigm, which has performance limitations when the downstream task objectives differ from the pretraining objectives. To address this issue, this paper adopts a prompt learning-based model. By designing task-description-based prompt templates, the downstream task is reformulated as a masked language prediction task, making full use of the semantic understanding of pre-trained language models. Experimental results show that compared to existing fine-tuning methods, the prompt learning-based approach improves accuracy by 6.8%-7.1% on the Douban movie review dataset, demonstrating the excellent performance of the prompt learning paradigm.
Keywords
Prompt learning, Movie reviews, Sentiment analysis
[1]. Kaplan A M, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media[J]. Business horizons, 2010, 53(1): 59-68.
[2]. Pang B, Lee L. Opinion mining and sentiment analysis[J]. Found. Trends Inf. Retr., 2007, 2(1-2): 1-135.
[3]. Hu M, Liu B. Mining and summarizing customer reviews[C]. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2004, 168-177.
[4]. Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques[C].Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, 2002, 79–86.
[5]. KIM Y. Convolutional Neural Networks for Sentence Classification[C]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, ACL, 2014, 1746–1751.
[6]. Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences[C]. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, ACL, 2014, 655–665.
[7]. Liu P, Qiu X, Huang X. Recurrent neural network for text classification with multi-task learning[C]. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, IJCAI/AAAI Press, 2016, 2873–2879.
[8]. Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification[C]. Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, ACL, 2015, 1422–1432.
[9]. X. Chen and X. Luo, "Optimization of NO annealing process for enhanced SiC MOSFET gate oxide reliability," IEEE Electron Device Letters, vol. 44, no. 7, pp. 1156–1159, 2023.
[10]. Radford A, Narasimhan K, Salimans T, Sutskever I. Improving language understanding by generative pre-training. OpenAI Technical Report, 2018.
[11]. Liu P, Yuan W, Fu J, et al. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing[J]. ACM Computing Surveys, 2023, 55(9): 1-35.
[12]. Nedjah N, Santos I, de Macedo Mourelle L. Sentiment analysis using convolutional neural network via word embeddings[J]. Evolutionary Intelligence, 2022, 15(4): 2295-2319.
[13]. Liu Z, Zhang D, Luo G, et al. A new method of emotional analysis based on CNN–BiLSTM hybrid neural network[J]. Cluster Computing, 2020, 23: 2901-2913.
[14]. Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]. Proceedings of the 38th International Conference on Machine Learning, ICML 2021, PMLR, 2021, 8748-8763.
[15]. Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer[J]. Journal of machine learning research, 2020, 21(140): 1-67.
[16]. Schick T, Schütze H. Exploiting cloze questions for few shot text classification and natural language inference[C]. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, ACL, 2021, 255-269.
[17]. Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing: A survey[J]. Science China technological sciences, 2020, 63(10): 1872-1897.
[18]. Schick T, Schütze H. Exploiting cloze questions for few shot text classification and natural language inference[C]. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, ACL, 2021, 255-269.
Cite this article
Zhang,Y. (2025). Sentiment Analysis Method for Douban Movie Reviews Based on Prompt Learning. Applied and Computational Engineering,151,183-191.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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