Sentiment Analysis of Japanese Tourism Online Reviews

Abstract

Purpose: Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4Travel.Design/methodology/approach: Different statistics-and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. We also make seven groups of different rulesbased methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown. Findings:We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism (EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.Research limitation: Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods. Practical implications:The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.

Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies

“…Some researchers have shown that online reviews have a significant impact on the tourism business [51][52][53]. Some researchers have conducted sentiment analysis on tourism online reviews [54][55][56]. These studies usually utilized sentiment analysis to classify words or features in the reviews into positive, negative, or neutral.…”

Application of Data Engineering in Automatic information analysis for Electronic Customer Relationship Management: A Survey

“…This valuable information is gold for many travelers. These reviews allow travelers to know in advance the characteristics of the tourist sites and thus avoid problems during their trip or their stay in a certain place [79].…”

“…Yu and collaborators automatically extracted reviews of Chinese tourist attractions from a Japanese travel website called 4Travel [79]. The findings of these researchers can provide practical guidelines for use within the field of sentiment analysis from Japanese online tourism reviews and facilitate the improvement of tourist attraction service.…”

Electronic Customer Relationship Management in action: an example in business

“…The combination of tourism and the Internet (Internet + Travel) allows people to obtain more detailed travel information. Yu and collaborators automatically extracted reviews of Chinese tourist attractions from a Japanese travel website called 4Travel [76]. In Law and colleagues' research, user searches are used to forecast tourism demand in Macao [77].…”