| 基于联合分类器过滤噪声的微博主题发现 |
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| 引用本文： | 高森,严曙,崔超远,孙丙宇,汪六三. 基于联合分类器过滤噪声的微博主题发现[J]. 计算机系统应用, 2018, 27(1): 132-136 |
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| 作者姓名： | 高森 严曙 崔超远 孙丙宇 汪六三 |
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| 作者单位： | 中国科学院 合肥物质科学研究院 智能机械研究所, 合肥 230031;中国科学技术大学, 合肥 230026,中国科学院 合肥物质科学研究院 智能机械研究所, 合肥 230031,中国科学院 合肥物质科学研究院 智能机械研究所, 合肥 230031,中国科学院 合肥物质科学研究院 智能机械研究所, 合肥 230031,中国科学院 合肥物质科学研究院 智能机械研究所, 合肥 230031 |
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| 基金项目： | 中科院STS项目（KFJ-SW-STS-144）；宁夏科技攻关项目（ZNNFKJ2015-04） |
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| 摘 要： | 伴随着互联网的广泛流行，以微博为代表的社交网络产生了大量的数据. 从这些数据中挖掘到有用的信息成为当今研究的一项重要方向. 根据微博文本的特点，本文提出来一种基于联合分类器过滤掉噪声微博，然后利用LDA模型进行主题发现. 联合分类器模型是由朴素贝叶斯、支持向量机和决策树三种模型通过简单投票机制结合构成的，实验结果联合分类器的准确度达到87%，显然这种分类方法是可行的，也是有效的.
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| 关 键 词： | 支持向量机 朴素贝叶斯 决策树 联合分类器 LDA模型 |
| 收稿时间： | 2017-04-06 |
| 修稿时间： | 2017-04-26 |
Microblogging Theme Discovery Based on Combined Classifier Filtering Noise |
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| GAO Sen,YAN Shu,CUI Chao-Yuan,SUN Bing-Yu and WANG Liu-San. Microblogging Theme Discovery Based on Combined Classifier Filtering Noise[J]. Computer Systems& Applications, 2018, 27(1): 132-136 |
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| Authors: | GAO Sen YAN Shu CUI Chao-Yuan SUN Bing-Yu WANG Liu-San |
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| Affiliation: | Intelligent Machinery Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;University of Science and Technology of China, Hefei 230026, China,Intelligent Machinery Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China,Intelligent Machinery Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China,Intelligent Machinery Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China and Intelligent Machinery Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China |
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| Abstract: | With the popularity of the Internet, microblogging as a representative of the social network has generated a lot of data. Exploring useful information from these data has become an important direction for today''s research. According to the characteristics of microblogging text, this paper presents a method based on joint classifier to filter out noise microblogging, and then uses LDA model for subject discovery. The joint classifier model is composed of naive Bayesian, support vector machine and decision tree. The accuracy of the combined classifier is 87%, which can clearly show that this classification method is feasible and effective. |
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| Keywords: | support vector machine naive Bayesian decision tree joint classifier LDA model |
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