Set-Similarity joins based semi-supervised sentiment analysis

Authors:
Xishuang Dong;Qibo Zou;Yi Guan
Affiliations:
Web Intelligence Lab, Research Center of Language Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Web Intelligence Lab, Research Center of Language Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Web Intelligence Lab, Research Center of Language Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

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Abstract

A set-similarity joins based semi-supervised approach is presented to mine Chinese sentiment words and sentences. The set-similarity joins is taken to join nodes in unconnected sub-graphs conducted by cutting the flow graph with Ford-Fulkerson algorithm into positive and negative sets to correct wrong polarities predicted by min-cut based semi-supervised methods. Experimental results in digital, entertainment, and finance domains demonstrate the effectiveness of our proposed approach.