LAUSR

Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA and ACSA are created. ATSA: Aspect Term Sentiment Analysis Dataset. ACSA: Aspect Category Sentiment Analysis Dataset. In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines capsule network and BRET, and proposes the baseline model CapsNet-BERT. The experimental results verify the effectiveness of the model.