| 主成分分析与BP神经网络在微博舆情预判中的应用 |
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| 引用本文： | 饶浩,陈海媚. 主成分分析与BP神经网络在微博舆情预判中的应用[J]. 现代情报, 2016, 36(7): 58. DOI: 10.3969/j.issn.1008-0821.2016.07.011 |
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| 作者姓名： | 饶浩 陈海媚 |
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| 作者单位： | 1. 韶关学院信息管理系, 广东 韶关 512005;2. 韶关学院应用数学系, 广东 韶关 512005 |
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| 基金项目： | 2013年教育部人文社会科学研究青年基金项目“社交媒体潜在舆情发现及导控机制研究”（项目编号：13YJCZH144）研究成果之一。 |
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| 摘 要： | 首先通过主成分分析消除原始指标之间的相关性，使指标数量变少且相互之间不相关，从而构建综合预判指标，再利用BP神经网络建立微博舆情预判模型。实验选取2013年微博热门话题作为训练样本，选取2014年的话题作为预测。实验结果表明，主成分分析有助于去除原始样本数据的冗余，简化了网络的复杂度，所得到的结果更加准确。因此，该模型较仅使用BP神经网络的准确性更高。
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| 关 键 词： | 主成分分析 BP神经网络 微博舆情 预判模型 |
Application of Micro-blog Public Opinion Prediction Based on Combination of Principal Component Analysis and BP Neural Network |
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| Rao Hao,Chen Haimei. Application of Micro-blog Public Opinion Prediction Based on Combination of Principal Component Analysis and BP Neural Network[J]. , 2016, 36(7): 58. DOI: 10.3969/j.issn.1008-0821.2016.07.011 |
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| Authors: | Rao Hao Chen Haimei |
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| Affiliation: | 1. Department of Information Management, Shaoguan University, Shaoguan 512005, China;2. Department of Applied Mathematics, Shaoguan University, Shaoguan 512005, China |
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| Abstract: | Firstly,the paper reduced the correlation of original indicators by principal component analysis and the number of indicators and made them separate from each other.Secondly,it established comprehensive indicators for prediction.BP neural network is used to establish the model of micro-blog public opinion prediction.The experiment using 2013 micro-blog hot topics to predict 2014 hot topics.Experimental results showed that principal component analysis helped reduce redundancy of the statistical data and made predictions more accurate.This model was more accurate than the model using only BP neural network. |
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| Keywords: | principal component analysis BP neural network micro-blog public opinion prediction model |
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