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

被引：0

作者：

Xu, Ge ^{[1
]}

Wang, Houfeng ^{[2
]}

机构：

[1] MinJiang Univ, Dept Comp Sci, Fuzhou, Peoples R China

[2] Peking Univ, Inst Computat Linguist, Beijing, Peoples R China

来源：

2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS (WI-IAT WORKSHOPS 2012), VOL 3 | 2012年

关键词：

sentiment analysis; polarity classification; graph-based method;

D O I：

10.1109/WI-IAT.2012.18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For sentiment classification(1), 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.

引用

页码：132 / 136

页数：5