Abstract
In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a bidirectional long short-term memory network (Bi-LSTM) with a knowledge graph's synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. This data is then analyzed using a multi-layer gated recurrent unit (GRU) to pinpoint sentiment characteristics related to specific aspect terms. Tests on three widely available datasets demonstrate this method's superior performance in sentiment classification.
Abstract (translated)
在本文中，我们提出了一种新方法来增强情感分析，通过解决上下文特定单词含义的挑战。它将双向长短期记忆网络（Bi-LSTM）的优点与知识图的同义词数据相结合。这种协同作用利用了动态注意力机制开发了一个知识驱动的状态向量。对于将情感与特定方面相关联，该方法构建了一个记忆库，其中整合了位置数据。然后，该数据通过多层门控循环单元（GRU）进行分析，以确定与特定方面术语相关的情感特征。对三个广泛使用的数据集的测试表明，这种方法在情感分类方面的表现优于现有方法。
URL
https://arxiv.org/abs/2312.10048
