Paper Info
Title
Hidden sentiment association in chinese web opinion mining
Abstract
The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features as opposed to the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without the explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.
Year
DOI
Venue
2008
10.1145/1367497.1367627
WWW
Keywords
Field
DocType
opinion mining,chinese web opinion mining,product feature word,accurate opinion evaluation,finer-grained opinion mining,opinion word group,feature-level opinion mining,opinion word,different product feature,approach clusters product feature,feature-level opinion mining problem,hidden sentiment association,association
Data mining,World Wide Web,Computer science,Sentiment analysis,Product reviews,Data objects,Cluster analysis,Boom
Conference
Citations 
PageRank 
References 
105
3.45
18
Authors
8
Search Limit
100105
Name
Order
Citations
PageRank
Qi Su11053.45
Xinying Xu212914.79
Honglei Guo326514.36
Zhili guo426412.46
Xian Wu549536.50
Xiaoxun Zhang633616.74
Bin Swen71456.88
Zhong Su82282110.39