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

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