Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02041
Number of page(s) 10
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802041
Published online 17 November 2023
  1. Zhang, C. Research on IMDB Film Score Prediction Based on Improved Whale Algorithm. Procedia Computer Science, 208, 361–366. (2022). https://doi.org/https://doi.org/10.1016/j.procs.2022.10.051 [CrossRef] [Google Scholar]
  2. Samsir, S., Kusmanto, K., Dalimunthe, A. H., Aditiya, R., & Watrianthos, R. Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database. Building of Informatics, Technology and Science (BITS), 4(1 SE-Articles). (2022). https://doi.org/10.47065/bits.v4i1.1468 [Google Scholar]
  3. Kumar, K., Harish, B. S., & Darshan, H. K. Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method. International Journal of Interactive Multimedia and Artificial Intelligence, 5(5), 109. (2019). https://doi.org/10.9781/ijimai.2018.12.005 [CrossRef] [Google Scholar]
  4. Pratista, H. Memahami Film Edisi 2. Montase Press. (2017). [Google Scholar]
  5. Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. Learning from Imbalanced Data Sets. In Learning from Imbalanced Data Sets. (2018). https://doi.org/10.1007/978-3-319-98074-4 [Google Scholar]
  6. Iskandar, J. W., & Nataliani, Y. Perbandingan Naïve Bayes, SVM, dan kNN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6). (2021). https://doi.org/10.29207/resti.v5i6.3588 [Google Scholar]
  7. Liu, B. Sentiment analysis: Mining opinions, sentiments, and emotions. In Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. (2015). https://doi.org/10.1017/CBO9781139084789 [Google Scholar]
  8. W. Berry, M. Survey of Text Mining Clustering, Classification, and Retrieval Scanned by Velocity. In Fenxi Huaxue (Vol. 32, Issue 10). (2004). [Google Scholar]
  9. Wang, L. Support vector machines: theory and applications. Springer Science & Business Media. (2005). [CrossRef] [Google Scholar]
  10. Gorunescu, F. Data mining: Concepts, models and techniques. Intelligent Systems Reference Library, 12. (2011). https://doi.org/10.1007/978-3-642-19721-5 [Google Scholar]
  11. Campbell, C., & Ying, Y. Learning with support vector machines. In Synthesis Lectures on Artificial Intelligence and Machine Learning (SLAIML). Morgan &Claypool Publishers. (2011). [Google Scholar]

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