Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning
This work addresses the challenge of automating comparative opinion mining for consumer insights, though it appears incremental as it builds on existing extraction tasks with a novel method.
The paper tackled the problem of extracting comparative information from product reviews by proposing MTP-COQE, an end-to-end model using multi-perspective prompt-based learning, which achieved a 1.41% higher F1 score than previous baselines on an English dataset.
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.