CAJ | 학술논문

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。
침대목전호련망“부신식화”현상，제출료기우궤기학습적망락열점화제예측적사상。해사상통과총결능진량준학묘술열점화제적일조특정，득도매편신문각자적특정향량，병침대대량근기이지시부열문적수궤신문양본내용진행취류처리。기우건장정준적분류산법，이용지지향량궤장향량영사도고유공간체도분류목적。재궤기학습과정중，채용대량시험적방법수개병완선특정향량적조성、도량급권중，최종체도준학작출열점화제예측적목적。
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.