カンファレンス (国際) What Makes a Review Encouraging: Feature Analysis of User Access Logs in a Large-scale Online Movie Review Site
Kakeru Ito (Aoyama Gakuin univ.), Yoshiyuki Shoji (Aoyama Gakuin univ.), Sumio Fujita, Martin J. Dürst (Aoyama Gakuin univ.)
The 23rd International Conference on Information Integration and Web Intelligence (iiWAS 2021)
This paper reveals the characteristics of the reviews that encourage readers to watch the reviewed movie by analyzing large-scale access log data. We assume that some of the reviews that users saw just before they clicked the links to a streaming site contain factors that help users decide whether they watch that movie. Our method used a random forest classifier trained to determine whether a review encouraged a movie-watching behavior.We conducted feature importance-based analysis using three types of features: review itself, item, and reviewer. We analyzed 70,000 user behaviors from Yahoo! Movies (a movie review site in Japan) and Gyao! (a movie streaming site in Japan). Through a cross-validation experiment, the classifier was able to classify encouraging reviews with an Fscore of 0.78, and mainly the features about the item contributed to the classification performance. An additional subjects experiment confirmed that these features contribute to the review’s usefulness.