Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis
Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis
复制标题
用于维度情感分析的树结构区域 CNN-LSTM 模型
DOI：
10.1109/taslp.2019.2959251
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Zhang, Xuejie
中科院分区：
文献类型：
--
作者：
Wang, Jin;Yu, Liang-Chih;Zhang, Xuejie
维度情感分析旨在识别多个维度中的连续数值，例如效价唤醒（VA）空间。与专注于诸如二元分类（即，积极和消极），维度方法可以提供更细粒度的情感分析。本文提出了一种树结构的区域CNN-LSTM模型，该模型由区域CNN和LSTM两部分组成，用于预测文本的VA评级。与传统的CNN将整个文本作为输入不同，所提出的区域CNN使用文本的一部分作为区域，将输入文本划分为几个区域，以便可以提取每个区域中的有用情感信息并根据它们对VA预测的贡献进行加权。这些区域信息使用LSTM跨区域顺序集成以进行VA预测。通过结合区域CNN和LSTM，可以在预测过程中考虑句子内的局部（区域）信息和句子之间的长距离依赖关系。为了进一步提高性能，提出了一种区域划分策略，以发现任务相关的短语和子句，将结构化信息纳入VA预测。在不同语料库上的实验结果表明，该方法的性能优于词典、回归、传统神经网络和其他结构化神经网络方法。
Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valence-arousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide a more fine-grained sentiment analysis. This article proposes a tree-structured regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses a part of the text as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependencies across sentences can be considered in the prediction process. To further improve performance, a region division strategy is proposed to discover task-relevant phrases and clauses to incorporate structured information into VA prediction. Experimental results on different corpora show that the proposed method outperforms lexicon-, regression-, conventional NN and other structured NN methods proposed in previous studies.