Identification of Opinion and Ground in Customer Review Using Heterogeneous Datasets

Abstract

Online reviews are a valuable source of information for both potential buyers and enterprises, but not all reviews provide us helpful information. This paper aims at the identification of a user’s opinion and its reason or ground in a review, supposing that a review including a ground for an opinion is helpful. A classifier to identify an opinion and a ground, called the opinion-ground classifier, is trained from three heterogeneous datasets. The first is the existing dataset for discourse analysis, KWDLC, which is the manually labeled but out-domain dataset. The second is the in-domain but weakly supervised dataset made by a rule-based method that checks the existence of causality discourse markers. The third is another in-domain dataset augmented by ChatGPT, where a prompt to generate new samples is given to ChatGPT. We train several models as the opinion-ground classifier. Results of our experiments show that the use of automatically constructed datasets significantly improves the classification performance. The F1-score of our best model is 0.71, which is 0.12 points higher than the model trained from the existing dataset only.