Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning

TL;DR

This paper introduces MTP-COQE, an end-to-end prompt-based learning model that improves the extraction of comparative opinions from product reviews, demonstrating superior performance and addressing challenges like nuanced language and data imbalance.

Contribution

The paper presents a novel multi-perspective prompt-based approach for comparative opinion mining, enhancing extraction accuracy and robustness over traditional methods.

Findings

Achieved 1.41% higher F1 score on English dataset.

Effectively limited model creativity to improve output quality.

Utilized data augmentation to handle data imbalance.

Abstract

Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparative information from product reviews is challenging due to nuanced language and sequential task errors in traditional methods. To mitigate these problems, we propose MTP-COQE, an end-to-end model designed for COQE. Leveraging multi-perspective prompt-based learning, MTP-COQE effectively guides the generative model in comparative opinion mining tasks. Evaluation on the Camera-COQE (English) and VCOM (Vietnamese) datasets demonstrates MTP-COQE's efficacy in automating COQE, achieving superior performance with a 1.41% higher F1 score than the previous baseline models on the English dataset. Additionally, we designed a strategy to limit the generative model's creativity to ensure the output meets expectations. We also performed data augmentation to address data imbalance and to prevent the model from becoming biased towards dominant samples.