Cross-Cultural Examination on Content Bias and Helpfulness of Online Reviews: Sentiment Balance at the Aspect Level for a Subjective Good

Nakayama, Makoto
Wan, Yun
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Online reviews can be fraught with biases, especially on experience goods. Using multilingual sentiment analysis software, we examined the characteristics of review biases and helpfulness at the aspect level across two different cultures. First, we found the lopsidedness of emotions expressed over the four key aspects of Japanese restaurant reviews between Japanese and Western consumers. Second, helpful reviews have sentiments expressed more evenly over those aspects than average for both Japanese and Western consumers. Third, however, there are significant differences over how sentiments are spread over aspects between them. Westerners found reviews helpful when reviews focused less on food and more on service. In addition, Japanese customers were more concerned with savings whereas Westerners paid attention to whether they are getting their money’s worth. These findings point to future research opportunities for leveraging sentiment analysis over key aspects of goods, particularly those of experience/subjective goods, across different cultures and customer profile categories.
Data, Text, and Web Mining for Business Analytics, Decision Analytics, Mobile Services, and Service Science, cross-cultural comparison, online restaurant review, review aspect, review bias, sentiment analysis
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