Using Multiple Resources in Graph-Based Semi-supervised Sentiment Classification

Authors:
Ge Xu;Houfeng Wang
Affiliations:
-;-
Venue:
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2012

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Abstract

For sentiment classification, there exist a heterogeneous mass of resources such as semantic dictionaries, unlabeled corpora, and heuristic rules. In this paper, based on a graph-based semi-supervised algorithm, we focus on exploiting multiple resources to construct similarity matrices which are fused by simple but effective schemes. We reported encouraging results of the experiments in sentiment classification, which indicate that the adopted algorithm can utilize multiple resources to improve performance.