Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method

  • D. Diffran Nur Cahyo Magister Teknik Informatika, Universitas Amikom Yogyakarta
  • Fidya Farasalsabila Universitas Amikom Yogyakarta
  • Verra Budhi Lestari Universitas Amikom Yogyakarta
  • Hanafi Informatics Department, Universitas Amikom Yogyakarta
  • Tutik Lestari Informatics Department, Institut Teknologi Tangerang Selatan
  • Fahmi Rusdi Al Islami Informatics Department, Institut Teknologi Tangerang Selatan
  • M. Akbar Maulana Informatics Department, Institut Teknologi Tangerang Selatan
Abstract views: 406 , PDF downloads: 311
Keywords: Sentiment Analysis, IMDb, Movie Review, TF-IDF, SVM

Abstract

Many researchers currently employ supervised, machine learning methods to study sentiment analysis. Analysis can be done on movie reviews, Twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. Support Vector Machines (SVM) classifiers are used to analyze the Twitter data set using different parameters. The analysis and discussion were undertaken to allow for the conclusion that SVM has been successfully implemented utilizing the IMDb data for this study (Support Vector Machine). To complete this study, the preprocessing phase, which consisted of filtering and classifying data using SVM with a total of 50.000 data points, was completed after collecting up to 40.000 reviews to use as training data and 10.000 reviews to use as testing data. 25.000 positive and 25.000 negative points make up the view. In this study, we adopted an evaluation matrix including accurate, precision, recall, and F1-score. According to the experiment report, our model achieved SVM with Bags of Word (BoW) used to get results for the highest accuracy test, which was 88,59% accurate. Then, using grid-search, optimize against the SVM parameters to find the best parameters that SVM models can use. Our model achieved Term Frequency–inverse Document Frequency (TF-IDF) was used to get results for the highest accuracy test, which was 91,27% accurate.

 

Author Biographies

D. Diffran Nur Cahyo, Magister Teknik Informatika, Universitas Amikom Yogyakarta

 

 

Hanafi, Informatics Department, Universitas Amikom Yogyakarta

 

 

 

 

Tutik Lestari, Informatics Department, Institut Teknologi Tangerang Selatan

 

 

Fahmi Rusdi Al Islami, Informatics Department, Institut Teknologi Tangerang Selatan

 

 

M. Akbar Maulana, Informatics Department, Institut Teknologi Tangerang Selatan

 

 

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Published
2023-03-18
How to Cite
Nur Cahyo, D. D., Farasalsabila, F., Lestari, V. B., Hanafi, Lestari, T., Al Islami, F. R., & Maulana, M. A. (2023). Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 8(2), 90-95. https://doi.org/10.25139/inform.v8i2.5700
Section
Articles