Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory

• Published in: Juita : jurnal informatika (Online) Vol. 11; no. 1; pp. 27 - 36
• Main Authors: Af'idah, Dwi Intan, Anggraeni, Puput Dewi, Rizki, Muhammad, Setiawan, Aji Bagus, Handayani, Sharfina Febbi
• Format: Journal Article
• Language: English; Indonesian
• Published: Universitas Muhammadiyah Purwokerto 06-05-2023
• Subjects: aspects-based sentiment analysis; bilstm; lstm; tourism
• ISSN: 2086-9398; 2579-8901
• DOI: 10.30595/juita.v11i1.15341

The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.