| Summary: | 碩士 === 國立臺灣科技大學 === 管理學院MBA === 106 === Online reviews are the important information resource for retailers to understand the actual using experience from consumers. However, with the amount of User-generated content (UGC) grows increasingly, retailers would suffer from the problem of information overload to extract the useful information. In order to find out the helpful reviews, the text mining and the machine learning techniques are wide applied to discovering the opinion contents of consumers as well as predicting the helpfulness of reviews in recent years.
This research collected data from one of the main cosmetics and skin care products review website in Taiwan, and utilize the novel technique in the field of ensemble learning called “XGBoost”, building the prediction model which take review popularity as criteria, searching the important terms that have significant effect to review popularity. And compare with the conventional linear regression technique of predictive performance. Then use the “Skip-gram model” technique of word vector modeling in the text mining field to discover the correlatively semantic terms, detecting the key reviewers with both the important terms and the semantic terms. Finally, comparing the performance of the proposed term selected method with the “single term selected method” and “random term selected method”.
The results indicated that XGBoost can further suggest better determinants which have a greater effect on review popularity. In the term selected methods, the proposed method outperforms the other two methods in terms of predicting the review popularity. The finding of this research would help retailers discover the key reviewers who can attract people’s attentions effectively with specific terms group for emphasizing the features of product and promoting the purchase intention of consumers.
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