Music review classification enhanced by semantic information

Authors:
Wei Zheng;Chaokun Wang;Rui Li;Xiaoping Ou;Weijun Chen
Affiliations:
School of Software, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China and Tsinghua National Laboratory for Information Science and Technology and Key Laboratory for Information System Security, Ministry of Educ ...;School of Software, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University
Venue:
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Year:
2011

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Abstract

In this paper, we put forward the semantic-based music review classification problem by illustrating application scenarios. In this problem, we aim to classify music reviews into categories according to their semantic meanings. A solution called the SEMEC model is proposed. In SEMEC, besides the music reviews, related semantic music information is also utilized. A heuristic SVM classification algorithm is proposed to build the classifiers. SEMEC makes use of the concept of entropy and the probability model to combine the results from different classifiers. Extensive experimental results show that SEMEC is effective and efficient.