Deep Semantic Frame-Based Deceptive Opinion Spam Analysis

@inproceedings{Kim2015DeepSF,
  title={Deep Semantic Frame-Based Deceptive Opinion Spam Analysis},
  author={Seongsoon Kim and Hyeokyoon Chang and Seongwoon Lee and Minhwan Yu and Jaewoo Kang},
  booktitle={CIKM},
  year={2015}
}
User-generated content is becoming increasingly valuable to both individuals and businesses due to its usefulness and influence in e-commerce markets. As consumers rely more on such information, posting deceptive opinions, which can be deliberately used for potential profit, is becoming more of an issue. Existing work on opinion spam detection focuses mainly on linguistic features such as n-grams, syntactic patterns, or LIWC. However, deep semantic analysis remains largely unstudied. In this… CONTINUE READING
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Background to framenet

  • C. Fillmore, C. Johnson, M. Petruck
  • International journal of lexicography, 16(3):235
  • 2003
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