Clustering Product Features in Opinion Mining
Hongfei Lin · Zhongwen xinxi xuebao · 2012
This paper focuses on clustering different feature expressions in product reviews into proper groups.In product reviews,the same features may have different expressions,e.g.appearance and design of a mobile phone actuallyindicate the same feature.Considering the fact that different expressions are always used with same sentimental words in a sentence,this paper first extracts product feature expressions and sentimental words in pairs to build a bipartite graph,and then adopts the Weight Normalized SimRank to compute similarity between different feature expressions in the bipartite graph,and finally optimizes the Bayesian classifier in Semi-Supervised Learning via the similarity.Experimental results show that the proposed method is valid.