  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.
