Implicit Feature Identification via Co-occurrence Association Rule Mining

Abstract

In sentiment analysis, identifying features associated with an opinion can help produce a finer-grained understanding of online reviews. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews. In this paper, we propose a novel two-phase co-occurrence association rule mining approach to identifying implicit features. Specifically, in the first phase of rule generation, for each opinion word occurring in an explicit sentence in the corpus, we mine a significant set of association rules of the form [opinion-word, explicit-feature] from a co-occurrence matrix. In the second phase of rule application, we first cluster the rule consequents (explicit features) to generate more robust rules for each opinion word mentioned above. Given a new opinion word with no explicit feature, we then search a matched list of robust rules, among which the rule having the feature cluster with the highest frequency weight is fired, and accordingly, we assign the representative word of the cluster as the final identified implicit feature. Experimental results show considerable improvements of our approach over other related methods including baseline dictionary lookups, statistical semantic association models, and bi-bipartite reinforcement clustering.