Automatic multi-way domain concept hierarchy construction from customer reviews

Abstract

Abstract A concept hierarchy is important for many applications to manage and analyze text corpora. In the literature, most previous hierarchy construction works are under the assumption that the semantic relations in the concept hierarchy can be extracted from a text corpus, which is not fully satisfied for short and informal texts, e.g. tweets and customer reviews. And many works utilize hierarchical clustering methods to get the final concept hierarchy, in which the resulting binary-tree form concept hierarchy cannot fit the demand in many applications. In this paper, we propose a general process for building a concept hierarchy from customer reviews with an appropriate depth. The process can be divided into three steps. First, all highly ranked topic words are extracted as concept words using a topic model. And a word sense disambiguation task is performed to derive the possible semantics of the words. Then, the distances between these words are computed by combining their contexts and relations in the WordNet. Finally, all words are organized using a modified multi-way hierarchical clustering method. In addition, a new concept hierarchy evaluation model is presented. Our approach is compared to approaches using hierarchical clustering methods on the Amazon Customer Review data set, and the results show that our approach can get higher similarity scores with the reference concept hierarchy.