References
[1]. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77). Sanibel Island, FL, USA: ACM.
[2]. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
[3]. Zou, H., Tang, X., Xie, B., & Liu, B. (2015). Sentiment classification using machine learning techniques with syntax features. In 2015 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 175-179). Las Vegas, NV, USA: IEEE.
[4]. Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527-6535.
[5]. Ni, X., Xue, G.-R., Ling, X., Yu, Y., & Yang, Q. (2007). Exploring in the weblog space by detecting informative and affective articles. In Proceedings of the 16th international conference on World Wide Web (pp. 281-290). Banff, Alberta, Canada: ACM.
[6]. Yang, C., Lin, K. H.-Y., & Chen, H.-H. (2007). Emotion classification using web blog corpora. In IEEE/WIC/ACM International Conference on Web Intelligence (WI’07) (pp. 275-278). Frmont, CA, USA: IEEE.
[7]. Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021). ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 115, 279-294.
[8]. Wang, P. et al. (2018). Concept and attention-based CNN for question retrieval in multi-view learning. ACM Transactions on Intelligent Systems and Technology, 9(4), 1-24.
[9]. Weissenbacher, D., Sarker, A., Paul, M. J., & Gonzalez-Hernandez, G. (2018). Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task (pp. 13-16). Brussels, Belgium: Association for Computational Linguistics.
[10]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[11]. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding.
[12]. Gao, J. (2021). Chinese sentiment classification model based on pre-trained BERT. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 1296-1300). Ottawa, ON, Canada: ACM.
[13]. Shao, Y., & Wang, L. (2022). GPSAttack: A unified glyphs, phonetics and semantics multi-modal attack against Chinese text classification models. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Padua, Italy: IEEE.
[14]. Yang, X., Yang, L., Bi, R., & Lin, H. (2019). A comprehensive verification of transformer in text classification. In Chinese Computational Linguistics (pp. 207-218). Cham: Springer International Publishing.
[15]. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
[16]. Pemarathna, R. (2019). Impact of Xiaohongshu on its user base and society: A review, 2(11).
[17]. Hoang, M., Bihorac, O. A., & Rouces, J. Aspect-based sentiment analysis using BERT.
[18]. Liu, Y. et al. (2019). RoBERTa: A robustly optimized BERT pretraining approach.
[19]. Zhao, L., Li, L., Zheng, X., & Zhang, J. (2021). A BERT based sentiment analysis and key entity detection approach for online financial texts. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1233-1238). Dalian, China: IEEE.
[20]. Zheng, J., Wang, J., Ren, Y., & Yang, Z. (2020). Chinese sentiment analysis of online education and internet buzzwords based on BERT. Journal of Physics: Conference Series, 1631(1), 012034.
[21]. Li, H., Ma, Y., Ma, Z., & Zhu, H. (2021). Weibo text sentiment analysis based on BERT and deep learning. Applied Sciences, 11(22), 10774.
[22]. Li, Z., Zhou, L., Yang, X., Jia, H., Li, W., & Zhang, J. (2023). User sentiment analysis of COVID-19 via adversarial training based on the BERT-FGM-BiGRU model. Systems, 11(3), 129.
Cite this article
Zhou,F. (2024). Sentiment analysis and implementation in film evaluation utilizing BERT. Applied and Computational Engineering,33,224-233.
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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References
[1]. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77). Sanibel Island, FL, USA: ACM.
[2]. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
[3]. Zou, H., Tang, X., Xie, B., & Liu, B. (2015). Sentiment classification using machine learning techniques with syntax features. In 2015 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 175-179). Las Vegas, NV, USA: IEEE.
[4]. Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527-6535.
[5]. Ni, X., Xue, G.-R., Ling, X., Yu, Y., & Yang, Q. (2007). Exploring in the weblog space by detecting informative and affective articles. In Proceedings of the 16th international conference on World Wide Web (pp. 281-290). Banff, Alberta, Canada: ACM.
[6]. Yang, C., Lin, K. H.-Y., & Chen, H.-H. (2007). Emotion classification using web blog corpora. In IEEE/WIC/ACM International Conference on Web Intelligence (WI’07) (pp. 275-278). Frmont, CA, USA: IEEE.
[7]. Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021). ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 115, 279-294.
[8]. Wang, P. et al. (2018). Concept and attention-based CNN for question retrieval in multi-view learning. ACM Transactions on Intelligent Systems and Technology, 9(4), 1-24.
[9]. Weissenbacher, D., Sarker, A., Paul, M. J., & Gonzalez-Hernandez, G. (2018). Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task (pp. 13-16). Brussels, Belgium: Association for Computational Linguistics.
[10]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[11]. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding.
[12]. Gao, J. (2021). Chinese sentiment classification model based on pre-trained BERT. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 1296-1300). Ottawa, ON, Canada: ACM.
[13]. Shao, Y., & Wang, L. (2022). GPSAttack: A unified glyphs, phonetics and semantics multi-modal attack against Chinese text classification models. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Padua, Italy: IEEE.
[14]. Yang, X., Yang, L., Bi, R., & Lin, H. (2019). A comprehensive verification of transformer in text classification. In Chinese Computational Linguistics (pp. 207-218). Cham: Springer International Publishing.
[15]. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
[16]. Pemarathna, R. (2019). Impact of Xiaohongshu on its user base and society: A review, 2(11).
[17]. Hoang, M., Bihorac, O. A., & Rouces, J. Aspect-based sentiment analysis using BERT.
[18]. Liu, Y. et al. (2019). RoBERTa: A robustly optimized BERT pretraining approach.
[19]. Zhao, L., Li, L., Zheng, X., & Zhang, J. (2021). A BERT based sentiment analysis and key entity detection approach for online financial texts. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1233-1238). Dalian, China: IEEE.
[20]. Zheng, J., Wang, J., Ren, Y., & Yang, Z. (2020). Chinese sentiment analysis of online education and internet buzzwords based on BERT. Journal of Physics: Conference Series, 1631(1), 012034.
[21]. Li, H., Ma, Y., Ma, Z., & Zhu, H. (2021). Weibo text sentiment analysis based on BERT and deep learning. Applied Sciences, 11(22), 10774.
[22]. Li, Z., Zhou, L., Yang, X., Jia, H., Li, W., & Zhang, J. (2023). User sentiment analysis of COVID-19 via adversarial training based on the BERT-FGM-BiGRU model. Systems, 11(3), 129.