《沙丘2》影评的情感分析：基于Naïve Bayes算法的公众舆论翻译研究
Sentiment Analysis of Dune: Part Two Movie Reviews: Study on Public Opinion Translation Based on Naïve Bayes Algorithm
DOI: 10.12677/ml.2025.138826, PDF,   
作者: 马欣欣：上海海事大学外国语学院，上海
关键词: 情感分析；《沙丘2》；Naïve Bayes；Sentiment Analysis； Dune: Part Two； Naïve Bayes
摘要: 情感分析是自然语言处理领域最重要的分支之一。本研究基于Naïve Bayes算法，针对《沙丘2》影评进行情感分析，旨在揭示公众对影片的情感倾向，为电影的字幕翻译提供参考。通过对来自IMDb网站的1065条影评数据进行预处理、特征提取和情感分类，本研究实现95%的准确率、精确度和召回率。研究结果表明，Naïve Bayes算法在情感分类任务中表现出色，有效区分了正面、负面和中立的情感态度。这一分析不仅为电影研究领域提供了有价值的反馈，也为电影字幕制作和营销提供了实际的舆情参考。
Abstract: Sentiment analysis stands as one of the most crucial branches within the field of natural language processing. This study employs the Naïve Bayes algorithm to conduct sentiment analysis on reviews of Dune: Part Two, aiming to uncover public sentiment towards the film and provide a reference framework for its subtitle translation. By preprocessing, extracting features, and classifying the sentiment of 1065 review samples sourced from IMDb, this research achieved high performance metrics of 95% accuracy, precision, and recall. The results demonstrate that the Naïve Bayes algorithm excels in sentiment classification tasks, effectively distinguishing among positive, negative, and neutral attitudes. This analysis not only offers valuable feedback for the film studies domain but also provides practical insights derived from public opinion for movie subtitle production and marketing strategies.
文章引用：马欣欣. 《沙丘2》影评的情感分析：基于Naïve Bayes算法的公众舆论翻译研究[J]. 现代语言学, 2025, 13(8): 234-243. https://doi.org/10.12677/ml.2025.138826

参考文献

[1] 张紫琼, 叶强, 李一军. 互联网商品评论情感分析研究综述[J]. 管理科学学报, 2010, 13(6): 84-96.
[2] 孙长智, 易铭瑒, 蒋京京, 等. 基于深度主题自编码器模型的电影短评数据情感分析研究[J]. 情报科学, 2024, 42(11): 69-75.
[3] 涂小琴. 基于Python爬虫的电影评论情感倾向性分析[J]. 现代计算机(专业版), 2017(35): 52-55.
[4] 张璐. 从Python情感分析看海外读者对中国译介文学的接受和评价: 以《三体》英译本为例[J]. 外语研究, 2019, 36(4): 80-86.
[5] Noviansyah, B., Effendi, M.M. and Achmad, Y. (2024) Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion. Journal of Computer Networks, Architecture and High Performance Computing, 6, 1406-1414. [Google Scholar] [CrossRef] 
[6] Wongkar, M. and Angdresey, A. (2019) Sentiment Analysis Using Naive Bayes Algorithm of the Data Crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, 16-17 October 2019, 1-5. [Google Scholar] [CrossRef] 
[7] Turmudi Zy, A., Nugroho, A., Rivaldi, A. and Afriantoro, I. (2022) Analisis Sentimen Terhadap Pembobolan Data Pada Twitter Dengan Algoritma Naive Bayes. Jurnal Teknologi Informatika Dan Komputer, 8, 202-213. [Google Scholar] [CrossRef] 
[8] Pristiyono, Ritonga, M., Ihsan, M.A.A., Anjar, A. and Rambe, F.H. (2021) Sentiment Analysis of COVID-19 Vaccine in Indonesia Using Naïve Bayes Algorithm. IOP Conference Series: Materials Science and Engineering, 1088, Article 012045. [Google Scholar] [CrossRef] 
[9] Darmawan, S., Agusvina, N., Lusa, S. and Sensuse, D.I. (2023) Knowledge Management Factors and Its Impact on Organizational Performance: A Systematic Literature Review. JOIV: International Journal on Informatics Visualization, 7, 161-167. [Google Scholar] [CrossRef] 
[10] Yuyun, Hidayah, N. and Sahibu, S. (2021) Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5, 820-826. [Google Scholar] [CrossRef] 
[11] Nurtikasari, Y., Alam, S. and Hermanto, T.I. (2022) Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes. INSOLOGI: Jurnal Sains Dan Teknologi, 1, 411-423. [Google Scholar] [CrossRef] 
[12] Esa Tiffani, I. (2020) Optimization of Naïve Bayes Classifier by Implemented Unigram, Bigram, Trigram for Sentiment Analysis of Hotel Review. Journal of Soft Computing Exploration, 1, 1-7.
[13] Najjichah, H., Syukur, A. and Subagyo, H. (2019) Pengaruh Text Preprocessing Dan Kombinasinya Pada Peringkas Dokumen Otomatis Teks Berbahasa Indonesia. Jurnal Teknologi Informasi, 15, 1-11.
[14] Putra, F., Tahiyat, H.F., Ihsan, R.M., Rahmaddeni, R. and Efrizoni, L. (2024) Penerapan Algoritma K-Nearest Neighbor Menggunakan Wrapper Sebagai Preprocessing untuk Penentuan Keterangan Berat Badan Manusia. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4, 273-281. [Google Scholar] [CrossRef]