Convolutional Neural Networks for Indonesian Aspect-Based Sentiment Analysis Tourism Review

Abstract

In recent years, electronic word of mouth (e-WOM) has been widely used by people around the world. Tripadvisor is an e-WOM travel website that provides information about reviews and opinions on travel-related content. To help users gather information faster, aspect-based sentiment analysis is necessary. Aspect-based sentiment analysis helps users to capture and extract important features from the reviews. Therefore, this study aims to build an aspect-based sentiment analysis model of Indonesian tourism review by extracting aspect-category and their corresponding polarities from user reviews. To gain the best model, we performed several experiments by using Convolutional Neural Networks (CNN). Moreover, we compared our CNN model with CNN-LSTM and CNN-GRU to identify the sentiment and aspects from the reviews. We also performed negation handling in our feature extraction process to improve our CNN models. Based on our experiments, CNN combined with both POS tag and negation handling outperformed the other models with the accuracy of sentiment analysis of 0.9522 and aspect category of 0.9551.