.png){kind=icon}
返回
Lightweight multilayer interactive attention network for aspect-based sentiment analysis
.png){kind=icon}
OA
AI
DOI：10.1080/09540091.2023.2189119.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Aspect-based sentiment analysis (ABSA) aims to automatically identify the sentiment polarity of specific aspect words in a given sentence or document. Existing studies have recognised the value of interactive learning in ABSA and have developed various methods to precisely model aspect words and their contexts through interactive learning. However, these methods mostly take a shallow interactive way to model aspect words and their contexts, which may lead to the lack of complex sentiment information. To solve this issue, we propose a Lightweight Multilayer Interactive Attention Network (LMIAN) for ABSA. Specifically, we first employ a pre-trained language model to initialise word embedding vectors. Second, an interactive computational layer is designed to build correlations between aspect words and their contexts. Such correlation degree is calculated by multiple computational layers with neural attention models. Third, we use a parameter-sharing strategy among the computational layers. This allows the model to learn complex sentiment features with lower memory costs. Finally, LMIAN conducts instance validation on six publicly available sentiment analysis datasets. Extensive experiments show that LMIAN performs better than other advanced methods with relatively low memory consumption.
Keyword:
Sentiment analysis
deep learning
interactive learning
attention networks
期刊
IF:
3.4
论文数:
854
被引数:
1.5K
机构
引用论文
An emotional classification method of Chinese short comment text based on ELECTRA基于ELECTRA的中文短评文本情感分类方法.png)
CONNECTION SCIENCEOAAI
CONNECTION SCIENCEIF3.4
OAAI分享收藏
分享收藏Memory network with hierarchical multi-head attention for aspect-based sentiment analysisAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏
查看更多内容