Clustering product aspects using two effective aspect relations for opinion mining

Aspect recognition and clustering is important for many sentiment analysis tasks. To date, many algorithms for recognizing product aspects have been explored, however, limited work have been done for clustering the product aspects. In this paper, we focus on the problem of product aspect clustering. Two effective aspect relations: relevant aspect relation and irrelevant aspect relation are proposed to describe the relationships between two aspects. According to these two relations, we can explore many relevant and irrelevant aspects into two different sets as background knowledge to describe each product aspect. Then, a hierarchical clustering algorithm is designed to cluster these aspects into different groups, in which aspect similarity computation is conducted with the relevant aspect set and irrelevant aspect set of each product aspect. Experimental results on camera domain demonstrate that the proposed method performs better than the baseline without using the two aspect relations, and meanwhile proves that the two aspect relations are effective.