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.


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 rules-based 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.


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.


Our research is of practicability. Currently, there are no studies that focus on the sentiment analysis of Japanese reviews about Chinese attractions.

Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining