Dense Concatenation Memory Network for Aspect Level Sentiment Analysis
Gailing MA, Xiao Guo
Abstract
Aspect-level sentiment analysis is a fundamental task in sentiment analysis. It has many practical applications in product reviews, takeaway evaluations, and public opinion supervision. Over the past few years, previous studies have made remarkable progress. However, the existing methods still have some areas for improvement in aspect processing and feature fusion. In this study, we propose an aspect-context dense concatenation model (ACDC) to merge deep semantic information from various aspects and contexts. ACDC first captures the interaction information between the aspect and context using an interaction matrix. Subsequently, a dense concatenation mechanism is used to extract and integrate features at different levels. In addition, we introduce two loss functions to eliminate the interference information and overfitting. On the Restaurant2014 and Laptop2014 datasets, the experimental results show that our proposed model achieves state-of-the-art performance in terms of accuracy and MF1. Furthermore, comparative experiments are conducted to verify the effectiveness of the proposed model.