Semantic dependent word pairs generative model for fine-grained product feature mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
| Hi-index | 0.00 |
Opinion mining is of great significance in the analysis of user generated content. While there is some progress in supervised classification of opinion, the unsupervised learning of product features has drawn less attention. Unlike previous approaches based on basic syntactic pattern, our product feature mining utilizes syntactic dependency knowledge in a novel way by discriminating nominal and non-nominal terms. A nominal semantic structure will be parsed based on a dependency tree together with our model treating non-nominal terms as the semantic neighbors of the associated nominal terms. The semantic structure parsing will produce an opinionated pair stream with couples of nominal terms and its semantic neighbors, based on which fine-grained product features can be obtained by co-clustering approach via factorization method. Evaluation on average cluster entropies, perplexity and manual evaluation demonstrated advantage of our model. Product features highly cohesive in fine-grain are extracted automatically.