Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database


  • Samsir Samsir Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
  • Kusmanto Kusmanto Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
  • Abdul Hakim Dalimunthe Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
  • Rahmad Aditiya Teknik Informatika, Universitas Al-Washliyah Labuhanbatu, Indonesia
  • Ronal Watrianthos * Mail Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
  • (*) Corresponding Author
Keywords: Naïve Bayes; Sentiment Analysis; Internet Movie Database

Abstract

A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.

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Article History
Submitted: 2022-04-03
Published: 2022-06-27
Abstract View: 141 times
PDF Download: 163 times
How to Cite
Samsir, S., Kusmanto, K., Dalimunthe, A. H., Aditiya, R., & Watrianthos, R. (2022). Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database. Building of Informatics, Technology and Science (BITS), 4(1), 1-6. https://doi.org/10.47065/bits.v4i1.1468
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