Unveiling Comparative Sentiments in Vietnamese Product Reviews: A Sequential Classification Framework

TL;DR

This paper presents a sequential classification framework for extracting and understanding comparative sentiments in Vietnamese product reviews, addressing three sub-tasks to improve sentiment analysis accuracy.

Contribution

It introduces a novel three-step approach for identifying, extracting, and classifying comparative sentiments specifically in Vietnamese reviews, advancing language-specific sentiment analysis.

Findings

Ranked fifth at VLSP 2023 challenge

Effective identification of comparative sentences

Enhanced understanding of user sentiments in Vietnamese

Abstract

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.