| Abstract |
Reviews from customers on travel websites and platforms are quickly increasing. They provide people with the ability to write reviews sharing their experience with respect to service quality, location, room, or cleanliness, thereby helping others before booking hotels. Many people fail to consider hotel bookings because the numerous reviews take a long time to read, and many are in a non-native language. Thus, hotel businesses need an efficient process to analyze and categorize the polarity of reviews as positive, negative, or neutral. In particular, low-resource languages such as Thai have greater limitations in terms of resources to classify sentiment polarity. In this paper, a sentiment analysis method is proposed for Thai sentiment classification in the hotel domain. The Word2Vec technique is applied to create word embeddings of different vector dimensions. Two typical Word2Vec techniques (continuous bag of words (CBOW) and skip-gram approaches) were combined with Deep Learning (DL) models to observe the impact of each word vector dimension result. We compared the performance of nine DL models (CNN, LSTM, Bi-LSTM, GRU, Bi-GRU, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU) with different layers to evaluate their performance for polarity classification. Our experimental results show that the skip-gram and CNN model combination outperformed other DL models, reaching the highest accuracy of 0.9170. From the experiments, we found that the word vector dimensions, hyperparameter values, and layers of DL models affected the performance of sentiment classification. Our research provides guidance to set suitable hyperparameter values to improve the accuracy of sentiment classification for Thai language in the hotel domain. |
Publication
Journal Publication
ไทย