[1I2-J-5] Web Intelligence

[1I2-J-5-01] An Empirical Method to Remove Reviews against the Guidelines for Restaurant Review Sites

〇Yasutaka Shindoh¹, Atsunori Kanemura¹, Yusuke Miyao¹ (1. DG Lab, Digital Garage, Inc.)

Keywords:review, filtering, document classification

Restaurant reviews written by customers on the Web can influence many people when they decide what to eat. Offensive or irrelevant reviews are often posted to restaurant review services and they can make people displeased and ruin services' reputation. To avoid this, restaurant review service providers issue guidelines that define what are inappropriate reviews, and employ human workers to manually remove reviews violating the guidelines. Such manual operations incur high costs and automatic filtering is desirable. Unfortunately, although several filtering methods are available, their accuracy and efficiency are still not enough to work well on actual restaurant 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, and show through experiments on real restaurant review data that the method works well under practical and difficult situations.