该文研究了社会新闻中影响读者情感的深层特征。使用三种文本特征选择方法,分别从一元词、二元词和主题粒度下提取文本浅层特征,使用支持向量机模型选择三种粒度下最优浅层特征并且进行分类,得到最优宏平均F1值分别为60.5%、62.1%、63.3%。引入深度信念网络模型,使用三种粒度下最优浅层特征作为输入,进一步训练和抽象得到深层特征,实验中使用深度为3的深度信念网络模型进行训练与分类,最优宏平均F1值分别为61.4%、63.5%、66.1%。实验结果表明,深层特征比浅层特征具有更多的文本语义信息,可以更好地判断社会新闻对公众情绪影响。

This work investigates the deep features in social news which can influence the emotions of people.Three kinds of feature compression methods are used to extract shallow features from the granularities of unigram word,bigram word and theme.The work used Support Vector Machine to select the optimal shallow features of three granularities,and the optimal F1_macro are 60.5%、62.1% and 63.3% resepectirely. The work introduced Deep Belief Network (DBN) model to train and abstract the optimal shallow features, The optimal F1_macro of DBN3 are61.4%、63.5% and 66.1% respectively.The experimental results show that the deep features abstracted by Deep Belief Network have more semantic information and better performance than shallow features in determining the influence on peoples emotions by social news.