Unveiling Comparative Sentiments in Vietnamese Product Reviews: A Sequential Classification Framework
This work addresses a domain-specific problem for analyzing user sentiments in Vietnamese product reviews, but it is incremental as it builds on existing comparative opinion mining tasks.
The authors tackled comparative opinion mining in Vietnamese product reviews by proposing a sequential classification framework to identify comparative sentences, extract elements, and classify types, achieving a fifth-place ranking in the VLSP 2023 challenge.
Comparative opinion mining is a specialized field of sentiment analysis that aims to identify and extract sentiments expressed comparatively. To address this task, we propose an approach that consists of solving three sequential sub-tasks: (i) identifying comparative sentence, i.e., if a sentence has a comparative meaning, (ii) extracting comparative elements, i.e., what are comparison subjects, objects, aspects, predicates, and (iii) classifying comparison types which contribute to a deeper comprehension of user sentiments in Vietnamese product reviews. Our method is ranked fifth at the Vietnamese Language and Speech Processing (VLSP) 2023 challenge on Comparative Opinion Mining (ComOM) from Vietnamese Product Reviews.