A Study on Sentiment Analysis for Smart Tourism

Abstract

Sentiment analysis plays an indispensable role to help understand people’s opinions automatically based on their reviews. Existing research on sentiment analysis mainly focuses on film reviews, e-commerce reviews and other fields. These work cannot be applied to analyze the sentiment of travel reviews directly because the mainstream commodity review dataset is richer and more regular than that of travel review dataset. More specifically, the special characteristic of travel reviews makes existing solutions fail to achieve satisfactory results. To address this issue, we first construct a travel review data set for sentiment analysis. Then, we conduct a systematic study to investigate and compare the factors that may affect the accuracy of sentiment analysis for travel reviews. Based on the study findings, we design a lightweight Glove-BiLSTM-CNN model and BERT-BiLSTM-CNN to analyze the sentiment for travel reviews. Experimental results show that our proposed models outperform the baseline solutions.