6Attention-based LSTM for Aspect-level Sentiment Classification(2020.10.22)_joint attention lstm network for aspect-level sent-优快云博客

本文链接：https://blog.youkuaiyun.com/fuchengguo666/article/details/109218745

本文提出了一种Attention-based LSTM（ATAE-LSTM）模型，用于Aspect级别的情感分类。该模型结合了aspect嵌入和注意力机制，能够根据不同aspect关注句子的相应部分，从而实现对句子情感极性的精确判断。实验结果显示，ATAE-LSTM在SemEval2014数据集上表现出优越性能，优于标准LSTM、TD-LSTM和TC-LSTM。通过分析和案例研究，证明了模型在处理复杂句子结构和识别关键信息方面的能力。

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Attention-based LSTM for Aspect-level Sentiment Classification

基于注意的LSTM用于Aspect级别的情感分类

一、Abstract

Aspect-level sentiment classification is a fine-grained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years.
面向Aspect的情感分类是情感分析中的一项细粒度任务。由于Aspect级情感分析能够提供更全面、更深入的结果，近年来受到了广泛的关注。
In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect.
在本文中，我们揭示了句子的情感极性不仅由内容决定，而且与相关的Aspect高度相关。
For instance, “The appetizers are ok, but the service is slow”, for aspect taste, the polarity is positive while for service, the polarity is negative.
例如，“开胃菜还可以，但是服务很慢。”，对于特征‘taste’，极性为正极，而对于‘service’，极性为负极。
Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence.因此，‘aspect’与句子内容之间的联系是值得探讨的。
To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.
为此，我们提出了一种‘基于注意力’的‘LSTM网络’，用于方面级别的情感分类。
The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input.
当以不同的特征aspect作为输入时，注意力机制可以专注于句子的不同部分。
We experiment on the SemEval 2014 dataset and results show that our model achieves state-of-the-art performance on aspect-level sentiment classification.
我们在SemEval 2014数据集上进行了实验，