Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

In this study, we present a web-based application that retrieves hotel review documents in Indonesian languages from an online travel agent (OTA) and analyses their sentiments from the coarse-grained document to the fine-grained aspect level.

/Methodology/Approach: There are four main stages in this study: development of sentiment analysis model at the document level based on a convolutional neural network (CNN), development of sentiment analysis model at the aspect level based on an improved long short-term memory (LSTM), model deployment for multilevel sentiment analysis in a web-based application, and its performance evaluation. The developed application uses several sentiment visualizations types at coarse-grained and fine-grained levels, such as pie charts, line charts, and bar charts.

The application's functionality was demonstrated in practice based on three datasets from three OTA websites, which were analyzed and evaluated based on several matrices, namely, the precision, recall, and F1-score. The results revealed that the performance for the F1-score was 0.95 ± 0.03, 0.87 ± 0.02, and 0.92 ± 0.07 for document-level sentiment analysis, aspect-level sentiment analysis, and aspect-polarity detection, respectively.

The developed application (Sentilytics 1.0) can analyze sentiment at document and aspect levels. The two levels of sentiment analysis are based on two models generated by fine-tuning CNN and LSTM models using specific architectures and domain data (Indonesian hotel reviews).