Exploiting flexible constrained K means clustering with word embedding for aspect phrase grouping
Aspect phrase grouping is an important task for aspect finding in sentiment analysis. Most existing methods for this task are based on a window context model, which assumes that the same aspect has similar co occurrence contexts. This model does not always work well in practice. In this paper, we develop a novel weighted context representation model based on semantic relevance, which exploits word embedding method to represent aspect phrase. And we encode the lexical knowledge as constraints with a degree of belief, and further propose a flexible constrained K means algorithm to cluster aspect phrases. Empirical evaluation shows that the proposed method outperforms existing state of the art methods.
Inf. Sci.