A Simple Method to Remove Reviews against Guideline for Online Review Services
Yasutaka Shindoh, Atsunori Kanemura, Yusuke Miyao · 2018
Reviews written by customers on the Web can influence many people when they decide what to do. Offensive or irrelevant reviews are often posted to review services (e.g. TripAdvisor1and Glassdoor2), and they can make people displeased and ruin services' reputation. To avoid this, review service providers issue guidelines that define what are inappropriate reviews and employ human workers to manually remove reviews violating guideline. Such manual operations incur high costs and human filtering results may vary; then, automatic filtering is desirable. Unfortunately, although several filtering methods are available, their accuracy and efficiency are still not enough to work well on actual review services because of their costs, complexities and reviews' noisiness. In this paper, we introduce a simple, accurate, and efficient method that detects whether a review violates guidelines or not, using logistic regression models with features based on word n-gram. We show through experiments on real review data that the method works well under practical and difficult situations. The method can be applied to any review services, and can eliminate the costs of manual operations.