Authors:
Po-Min Chuang
;
Kiyoaki Shirai
and
Natthawut Kertkeidkachorn
Affiliation:
Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai Nomi, Ishikawa, Japan
Keyword(s):
Opinion Mining, Ground of Opinion, Customer Review, Discourse Analysis, Weakly-Supervised Learning.
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.
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