| 基于GDBN网络的文本情感倾向分类算法 |
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| 引用本文： | 陈颖熙,廖晓东,苏例月,陶状.基于GDBN网络的文本情感倾向分类算法[J].计算机系统应用,2019,28(1):163-168. |
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| 作者姓名： | 陈颖熙 廖晓东 苏例月 陶状 |
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| 作者单位： | 福建师范大学光电与信息工程学院,福州,350007;福建师范大学光电与信息工程学院,福州350007;福建师范大学医学光电科学与技术教育部重点实验室福建省光子技术重点实验室,福州350007;福建师范大学福建省先进光电传感与智能信息应用工程技术研究中心,福州350007 |
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| 基金项目： | 省科技厅区域科技重大项目（2015H4007）；中央引导地方科技发展专项（2017L3009） |
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| 摘 要： | 情感倾向性分类是自然语言处理领域中的热门话题，它的一个重要应用是挖掘线上评论中的重要信息，掌握网络舆论走向，因此本文提出一种基于GDBN网络的文本情感倾向性分类算法.该算法通过引入遗传算法来改进深度置信网络模型中的隐层，使模型自行对隐单元个数寻优，取得当前模型的适宜值，并以此模型进行深层建模与特征提取.最后通过反向传播网络对提取到的特征进行情感倾向性分类.在多个文本数据集上进行实验验证，验证结果表明了本文算法的有效性.
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| 关 键 词： | 情感倾向性分类 寻优搜索 遗传算法 深度置信网络 |
| 收稿时间： | 2018/7/2 0:00:00 |
| 修稿时间： | 2018/7/27 0:00:00 |
Text Sentiment Classification Based on GDBN Neural Network |
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| CHEN Ying-Xi,LIAO Xiao-Dong,SU Li-Yue and TAO Zhuang.Text Sentiment Classification Based on GDBN Neural Network[J].Computer Systems& Applications,2019,28(1):163-168. |
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| Authors: | CHEN Ying-Xi LIAO Xiao-Dong SU Li-Yue and TAO Zhuang |
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| Affiliation: | College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China,College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Optoelectronic Science and Technology for Medicine(Ministry of Education) Cum. Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China;Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China,College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China and College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China |
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| Abstract: | Text sentiment classification is a hot topic in the field of natural language processing. One of its important applications is to dig out important information from online comments and grasp the trend of public opinion on the Internet. Therefore, this study proposes a method of text sentiment classification based on GDBN neural network. The algorithm improves the hidden layer in the DBN neural network by introducing genetic algorithm, which is of powerful global searching ability, and the algorithm optimizes the number of hidden units and obtains the appropriate value of the current model, then the modeling and feature extraction of this model. Finally, we can classify the extracted features of the BP neural network. By testing multiple data, the results show that the proposed algorithm is effective. |
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| Keywords: | text sentiment classification optimization search genetic algorithm deep belief networks |
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