Hidden sentiment association in chinese web opinion mining

  • Authors:

  • Qi Su;Xinying Xu;Honglei Guo;Zhili Guo;Xian Wu;Xiaoxun Zhang;Bin Swen;Zhong Su

  • Affiliations:

  • Peking University, Beijing, China;Peking University, Beijing, China;IBM China Research Lab, Beijing, China;IBM China Research Lab, Beijing, China;IBM China Research Lab, Beijing, China;IBM China Research Lab, Beijing, China;Peking University, Beijing, China;IBM China Research Lab, Beijing, China

  • Venue:
  • Proceedings of the 17th international conference on World Wide Web
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

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.