An Aspect-Category-Opinion-Sentiment Quadruple Extraction with Distance Information for Implicit Sentiment Analysis

Abstract

The aspect-category-opinion-sentiment (ACOS) quadruples play an essential role in implicit sentiment analysis. Considering the distances between aspects and opinions in sentences, a novel Distance-Extract-Classify-ACOS quadruple extraction method with distance information between aspects and opinions is proposed. Compared with Double-Propagation-ACOS, JET-BERT-ACOS, and Extract-Classify-ACOS quadruple extraction models, the recall and F1 scores of the Distance-Extract-Classify-ACOS quadruple extraction model respectively increase by 2.08%-35.81% and 1.47%-36.7% on the Restaurant-ACOS and Laptop-ACOS datasets. Using the extracted quadruples for implicit sentiment analysis, the performance of the LSTM, GRU, TextCNN, and BERT models significantly outperform these models with original sentences, aspects-opinions pairs, and aspects-categories-opinions triples on Restaurant-ACOS and Laptop-ACOS datasets.