A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining text and heuristics for cost-sensitive spam filtering
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Content based SMS spam filtering
Proceedings of the 2006 ACM symposium on Document engineering
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 16th international conference on World Wide Web
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Finding unusual review patterns using unexpected rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Nowadays it is very common for people to write online reviews of products they have purchased. These reviews are a very important source of information for the potential customers before deciding to purchase a product. Consequently, websites containing customer reviews are becoming targets of opinion spam. -- undeserving positive or negative reviews; reviews that reviewers never use the product, but is written with an agenda in mind. This paper aims to detect spam reviews by users. Characteristics of the review will be identified based on previous research, plus a new feature -- rating consistency check. The goal is to devise a tool to evaluate the product reviews and detect product review spams. The approach is based on multiple criteria: checking unusual review vs. rating patterns, links or advertisements, detecting questions and comparative reviews. We tested our system on a couple of sets of data and find that we are able to detect these factors effectively.