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Saputra, Aryasuta
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3610

Abstract

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.
Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3610

Abstract

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.
Co-Authors Tobing, Fenina Adline Twince