Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

Authors

  • Wardianto Wardianto universita Diponegoro Semarang
  • Farikhin Farikhin Universitas Diponegoro Semarang
  • Dinar Mutiara Kusumo Nugraheni Universitas Diponegoro Semarang

DOI:

https://doi.org/10.31328/jointecs.v8i2.4737

Keywords:

Tripadvisor, LSTM, ABSA, Adam Optimizer

Abstract

Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.

Author Biographies

Wardianto Wardianto, universita Diponegoro Semarang

Magister Sistem Informasi, Sekolah Pascasarjana

Farikhin Farikhin, Universitas Diponegoro Semarang

Program Studi Matematika, Fakultas Sains dan Matematika

Dinar Mutiara Kusumo Nugraheni, Universitas Diponegoro Semarang

Program Studi Teknik Informatika, Fakultas Sains dan Matematika,

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Published

2023-07-01

Issue

Vol. 8 No. 2 (2023)

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