Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
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DOI：
10.1609/aaai.v32i1.12048
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发表时间：
2018-04
影响因子：
4.7
通讯作者：
Yukun Ma;Haiyun Peng;E. Cambria
中科院分区：
文献类型：
--
作者：
Yukun Ma;Haiyun Peng;E. Cambria
分析人们对某些方面的看法和情感是自然语言理解的一项重要任务。在本文中，我们提出了一种新的基于方面的情感分析的解决方案，它通过利用常识来解决基于方面的情感分析和目标情感分析的挑战。我们在长短期记忆(LSTM)网络中引入了一种由目标层注意和句子层注意组成的分层注意机制。情感相关概念的常识知识被纳入用于情感分类的深度神经网络的端到端训练中。为了将常识知识紧密地结合到递归编码器中，我们提出了一种扩展的LSTM，称为Sentic LSTM。我们在两个公开发布的数据集上进行了实验，结果表明，所提出的注意结构和Sentic LSTM相结合的方法在目标方面情感任务中的性能优于最新的方法。
Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding. In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We augment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a target-level attention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into the end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense knowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments on two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic LSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks.