Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features

Sun Xiao; Li Chengcheng; Ren Fuji

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Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features

机译：基于具有卷积扩展特征的深度神经网络的中文微博情感分析

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摘要

Related research for sentiment analysis on Chinese microblog is aiming at the analysis procedure of posts. The length of short microblog text limits feature extraction of microblog. Tweeting is the process of communication with friends, so that microblog comments are important reference information for related post. A contents extension framework is proposed in this paper combining posts and related comments into a microblog conversation for features extraction. A novel convolutional auto encoder is adopted which can extract contextual information from microblog conversation as features for the post. A customized DNN (Deep Neural Network) model, which is stacked with several layers of RBM (Restricted Boltzmann Machine), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of input data to learn hidden structures for better high level features representation. A ClassRBM (Classification RBM) layer, which is stacked on top of RBM layers, is adopted to achieve the final sentiment classification label for the post. Experimental results show that, with proper structure and parameters, the performance of proposed DNN on sentiment classification is better than state-of-the-art surface learning models such as SVM or NB, which proves that the proposed DNN model is suitable for short-length document classification with the proposed feature dimensionality extension method. (C) 2016 Elsevier B.V. All rights reserved.

机译：针对中文微博情感分析的相关研究旨在针对帖子的分析过程。微博文本的长度限制了微博的特征提取。发推是与朋友交流的过程，因此微博评论是相关帖子的重要参考信息。本文提出了一种内容扩展框架，将帖子和相关评论组合到微博会话中以进行特征提取。采用了一种新颖的卷积自动编码器，该编码器可以从微博会话中提取上下文信息作为帖子的特征。实现了一个定制的DNN（深度神经网络）模型，该模型与多层RBM（受限玻尔兹曼机）堆叠在一起，用于初始化神经网络的结构。 RBM层可以采用输入数据的概率分布样本来学习隐藏结构，以获得更好的高级特征表示。采用堆叠在RBM层之上的ClassRBM（分类RBM）层，以实现职位的最终情感分类标签。实验结果表明，在适当的结构和参数的基础上，提出的DNN在情感分类上的性能优于SVM或NB等最新的表面学习模型，证明了该DNN模型适用于短时学习。提出的特征维数扩展方法对文档长度进行分类。（C）2016 Elsevier B.V.保留所有权利。

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来源
《Neurocomputing》 |2016年第19期|227-236|共10页
作者
Sun Xiao; Li Chengcheng; Ren Fuji;
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作者单位

Hefei Univ Technol, Sch Comp & Informat, TunXi Rd 193, Hefei 230009, Anhui, Peoples R China;

Hefei Univ Technol, Sch Comp & Informat, TunXi Rd 193, Hefei 230009, Anhui, Peoples R China;

Hefei Univ Technol, Sch Comp & Informat, TunXi Rd 193, Hefei 230009, Anhui, Peoples R China|Univ Tokushima, Fac Engn, Tokushima 7708506, Japan;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
DNN; Sentiment analysis; Microblog conversation; RBM; ClassRBM;

机译：DNN;情感分析;微博对话;RBM;ClassRBM;

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1. Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks [J] . Yuhai Yu, Hongfei Lin, Jiana Meng, Algorithms . 2016,第2期

机译：使用深度卷积神经网络的微博客视觉和文本情感分析
2. Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning [J] . Xu Feng, Zhang Xuefen, Xin Zhanhong, Computers, Materials & Continua . 2019,第3期

机译：基于卷积神经网络的深度学习中文文本情感分析研究
3. Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning [J] . Xu Feng, Zhang Xuefen, Xin Zhanhong, Computers, Materials & Continua . 2019,第3期

机译：基于深度学习卷积神经网络的中国文本情绪分析调查
4. Chinese microblog sentiment classification based on convolution neural network with content extension method [C] . Sun Xiao, Gao Fei, Li Chengcheng, 2015 International Conference on Affective Computing and Intelligent Interaction . 2015

机译：基于内容扩展方法的卷积神经网络的中文微博情感分类
5. Internal and External Feature Engineering Applied to Deep Learning with Convolutional Neural Networks for Monocular Relative Pose Estimation in Visual Odometry and Self-Localization [D] . Parkins, Franz Payton. 2020

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6. Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network [O] . Hongmei Li, Jinying Huang, Shuwei Ji 2019

机译：基于集成卷积神经网络和深度神经网络的特征融合方法进行轴承故障诊断
7. Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks [O] . Yuhai Yu, Hongfei Lin, Jiana Meng, 2016

机译：基于深度卷积神经网络的微博视觉和文本情感分析

Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features

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