Fatten Features and Drop Wastes: Finding Repeaters' Reviews by Feature Generation and Feature Selection

In this paper, we proposed a method for determining whether a given restaurant review comment is a repeater's review, or not. We often use restaurant review sites to decide which restaurant to go to. When we read a restaurant review comment, we can know whether the reviewer is a repeater of the restaurant. If a certain restaurant has many repeaters, the restaurant must be great. However, restaurant review sites usually do not provide a "revisit rate". Therefore, we tackle a problem for determining whether a review is a repeater's review, or not. There are many sentences in a review comment that are completely not useful for determining whether the review is a repeater review, such as what was ordered, what was delicious, or how was the price. To confront such difficulties, we have taken the following approach. First, very various features are extracted from review comments so as not to miss the features that represent repeaters' reviews. Next, from the very various features, only the necessary features that really contribute to the classification is selected by a feature selection method. Finally, classification is performed using a classifier. We have implemented the proposed method using super-CWC [12], a state-of-the-art feature selection method, and SVM. The experimental results show that the proposed method is better than other methods.