基于多维扩展特征与深度学习的微博短文本情感分析

孙晓; 彭晓琪; 胡敏; 任福继

doi:10.11999/JEIT160975

基于多维扩展特征与深度学习的微博短文本情感分析

doi: 10.11999/JEIT160975

1.
(情感计算与先进智能机器安徽省重点实验室合肥 230009) ②(德岛大学工学部德岛 770-8509)

基金项目:

国家自然科学基金(61432004)，模式识别国家重点实验室开放课题(NLPR)(201407345)，安徽省自然科学基金(1508085 QF119)，中国博士后科学基金(2015M580532)

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出版历程
- 收稿日期: 2016-09-28
- 修回日期: 2017-05-17
- 刊出日期: 2017-09-19

Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis

1.
(Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei 230009, China)

Funds:

The National Natural Science Foundation of China (61432004), The Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201407345), The Natural Science Foundation of Anhui Province (1508085QF119), The China Postdoctoral Science Foundation (2015M580532)

摘要

摘要: 该文提出了一种基于深度信念网络(DBN)和多维扩展特征的模型，实现对中文微博短文本的情感分类。为降低传统文本分类方法在处理微博短文时特征稀疏的影响，引入社交关系网络作为扩展特征，依据评论者和博主之间的社交关系，提取相关评论扩展原始微博，将扩展后的多维特征作为深度信念网络的输入。通过叠加多层玻尔兹曼机(RBM)构建DBN模型底层网络结构，多层玻尔兹曼机可以对原始输入抽象并获得数据的深层语义特征。在多个RBM层上叠加一层分类玻尔兹曼机(ClassRBM)，实现最终情感分类。实验结果表明，通过调整模型参数和网络结构，构建的深度学习模型在情感分类中能够获得比SVM和NB等浅层分类系统更优的结果，另外，实验证明使用扩展多维特征方法可提高短文本情感分类的性能。
- 社交网络 /
- 深度信念网络 /
- 扩展多维特征 /
- 受限玻尔兹曼机 /
- 分类受限玻尔兹曼机
Abstract: This paper presents a Deep Belief Nets (DBN) model and a multi-modality feature extraction method to extend features, dimensionalities of short text for Chinese microblogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the microblogging features according to the relationship between commenters and posters through constructing microblogging social network as input information. Multi-modality features are combined and adopted as the input vector for DBN. A DBN model, which is stacked with several layers of Restricted Boltzmann Machine (RBM), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of input data to learn hidden syntactic structures for better feature representation. A Classification RBM (ClassRBM) layer, which is stacked on top of the former RBM layers, is adapted to achieve the final sentiment classification. The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than the state of the art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.
- Social network /
- Deep Belief Nets (DBN) /
- Extended multi-modality features /
- Restricted Boltzmann Machine (RBM) /
- Classification restricted Boltzmann machine

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