Extracting Product Features from Reviews Using Feature Ontology Tree Applied on LDA Topic Clusters
Online product reviews provide data about the users perspective on the features that were experienced by them. Product features and corresponding opinions form a major part in analyzing the online product reviews. Extracting features from a huge number of reviews is categorized into three main categories such as utilizing language rules, sequence labeling and the topic modeling. Latent Dirichlet Allocation (LDA) is one such topic model which clusters the document words into unsupervised learned topics using Dirichlet priors. The words so clustered are the features and opinion words in the product reviews domain. These clusters contain words which are non features of the product. To identify appropriate product features from these clusters a hierarchical, domain independent Feature Ontology Tree (FOT) is applied to LDA clusters. This improves the accuracy of the features using extracted LDA topic clusters.
2016 IEEE 6th International Conference on Advanced Computing (IACC)