Learning to Identify Review Spam
Date
2011-07
Authors
Journal Title
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Publisher
AAAI Press / International Joint Conferences on Artificial Intelligence, Menlo Park, California
Abstract
In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem.
In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors’ products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features
in spam identification. We also observe that
the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a two-view
semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.
Description
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
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Conference Paper
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Keywords
SENTIMENT ANALYSIS, SPAM, PRODUCT REVIEWS, MACHINE LEARNING, MARKETING
Citation
Li, F., Huang, M., Yang, Y., & Zhu, X. (2011). Learning to Identify Review Spam. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, ES. (p. 2488-2493). AAAI / International Joint Conferences on Artificial Intelligence Press. doi:10.5591/978-1-57735-516-8/IJCAI11-414