| 基于机器学习的网络媒体热点话题预测方法研究与实现 | |
| 梁野 ; 郭宁宁 ; 李碧萩 ; 李超 ; 邢春晓 ; Liang Ye ; Guo Ningning ; Li Biqiu ; Li Chao ; Xing Chunxiao | |
| 2016-03-30 ; 2016-03-30 | |
| 关键词 | 机器学习 网络媒体 热点话题 特征向量 分词 预测 MCU TMS320DM8168 H264 G722 RTP TP181 TP391.1 |
| 其他题名 | Research and implementation of a forecasting method of hot topics in authoritative network media based on machine learning |
| 中文摘要 | 针对目前互联网"富信息化"现象,提出了基于机器学习的网络热点话题预测的思想。该思想通过总结能尽量准确描述热点话题的一组特征,得到每篇新闻各自的特征向量,并针对大量近期已知是否热门的随机新闻样本内容进行聚类处理。基于健壮精准的分类算法,利用支持向量机将向量映射到高维空间达到分类目的。在机器学习过程中,采用大量试验的方法修改并完善特征向量的组成、度量及权重,最终达到准确作出热点话题预测的目的。; Specific to the phenomenon of ″rich informationization″,an idea of Internet hot topic forecasting is proposed in this paper. The core of this idea is to summarize a set of relevant features of the hot topics in order to obtain the feature vectors of the sample news. Based on these features, therandom sample contents of a great deal of latest news are clustered, which means whether the news is a hot topic or not had been known to all. On the basis of theselected robust and accurate classification algorithm, the support vector machine is used to map the vectors into a higher dimensional space for the purpose of data classification. In the process of machine learning, the composition, the measurement and the weight of the feature vectors are modified and improved through trials and errors, thus to realize the accurate forecasting of hot topics. |
| 语种 | 中文 ; 中文 |
| 内容类型 | 期刊论文 |
| 源URL | [http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/146585]  |
| 专题 | 清华大学 |
| 推荐引用方式 GB/T 7714 | 梁野,郭宁宁,李碧萩,等. 基于机器学习的网络媒体热点话题预测方法研究与实现[J],2016, 2016. |
| APA | 梁野.,郭宁宁.,李碧萩.,李超.,邢春晓.,...&Xing Chunxiao.(2016).基于机器学习的网络媒体热点话题预测方法研究与实现.. |
| MLA | 梁野,et al."基于机器学习的网络媒体热点话题预测方法研究与实现".(2016). |
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