Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa

TL;DR

This paper introduces a multi-task learning model leveraging DeBERTa and a Local Context Focus mechanism to improve aspect-based sentiment analysis, demonstrating significant performance gains on benchmark datasets.

Contribution

The paper proposes integrating DeBERTa with a Local Context Focus mechanism into a multi-task learning framework for ABSA, showing enhanced accuracy over existing methods.

Findings

Significant improvement on SemEval-2014 laptop and restaurant datasets.

Enhanced performance on ACL Twitter dataset.

LCF mechanism effectively captures local context for better sentiment prediction.

Abstract

Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis, sen-tence-level sentiment analysis and aspect-level sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis, which aims to predict the polarity of aspects. The research of pre-training neural model has significantly improved the performance of many natural language processing tasks. In recent years, pre training model (PTM) has been applied in ABSA. Therefore, there has been a question, which is whether PTMs contain sufficient syntactic information for ABSA. In this paper, we explored the recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem. DeBERTa is a kind of neural language model based on transformer, which uses self-supervised learning to pre-train on a large number of original text corpora. Based on the Local Context Focus (LCF) mechanism, by integrating DeBERTa model, we purpose a multi-task learning model for aspect-based sentiment analysis. The experiments result on the most commonly used the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset show that LCF mechanism with DeBERTa has significant improvement.