J4 ›› 2012, Vol. 47 ›› Issue (3): 43-46.

• Articles • Previous Articles     Next Articles

Automobile domain oriented spam detection

TANG Du-yu1, WANG Da-liang2, ZHAO Kai2, QIN Bing1, LIU Ting1   

  1. 1. Research Center for Social Computing and Information Retrieval, School of Computer Science and Technology,
    Harbin Institute of Technology, Harbin 150001, Heilongjiang, China; 2. NEC Labs China, Beijing 100084, China
  • Received:2011-11-30 Online:2012-03-20 Published:2012-04-01


 The task that aims to detect spam reviews for the automobile domain was divided into four sub-tasks: supporting review detection, irrelevant review detection, advertisement detection and fake review detection. Both rule-based methods and machine learning methods were used to identify spam reviews. Many aspects were considered in the rule-based method, such as automobile domain knowledge, words with polarity, and information of the author. The review content feature and author information were combined to train a model with a maxent classifier. Experimental results showed that machine learning method performs well for the domain whose property was obvious, with numerical feedback information and labeled training data.

Key words:  spam detection; advertisement detection; rule-based method; machine learnin

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