Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis is a fine-grained sentiment analysis task that consists of two types of subtasks: aspect term extraction and aspect sentiment classification. In the aspect term extraction task, current methods suffer from the lack of fine-grained information in aspect term extraction and difficulty in identifying aspect term boundaries. In the aspect sentiment classification task, the current aspect sentiment classifier cannot adapt itself to the text and determine the local context. To address these two challenges, this work proposes an adaptive semantic relative distance approach based on dependent syntactic analysis, which uses adaptive semantic relative distance to determine the appropriate local context for each text and increase the accuracy of sentiment analysis. Meanwhile, the study also predicts the current word labels by combining local information features extracted by local convolutional neural networks and global information features to precisely locate the word labels. In two subtasks, our proposed model improves accuracy and F1 scores on the SemEval-2014 Task 4 Restaurant and Laptop datasets compared to the state-to-the-art approaches, especially in the aspect sentiment classification subtask.