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

文献类型： 外文期刊

第一作者： Huang, Jie

作者： Huang, Jie;Cui, Yunpeng;Huang, Jie;Cui, Yunpeng;Wang, Shuo

作者机构：

关键词： aspect term extraction; aspect sentiment classification; adaptive distance; local context

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 13 卷 1 期

页码：

收录情况： SCI

摘要： 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.

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