基于补全矩阵的多标签相关性情感分类

目前,对新闻情感分类问题的研究大部分是从新闻作者的角度进行的,而对读者反馈的真实情感分析的较少.本文从读者角度入手进行情感分析研究.提出一种基于补全矩阵的多标签相关性情感分类模型,采用LDA提取主题表示新闻文本,然后通过使用标签相关性矩阵对原始的标签矩阵进行补全,构造了一个增强的补全标签矩阵模型(CM-LDA).最后通过和原始矩阵的LDA模型进行比较,该模型使最终的多标签分类性能有了明显的提高,准确率达到了85.72%.

Emotion Classification of Multi-Label Correlation Based on Completion Matrix

At present, most of the researches of sentiment classification are carried out from the writer''s perspective with quite few analyses from readers.This paper is to study the sentiment analysis from the news readers.A model of multi-label correlation sentiment classification based on completion matrix and LDA is proposed to extract the topic.The original news text is represented with the generated text-subject features, which are taken as the input to a subsequent classifier.Furthermore, the paper constructs a model of enhanced completion label matrix (CM-LDA) by appending the label correlation matrix to the original label matrix.Results show that the accuracy of this approach achieves 85.72% in the multi-label classification task, which outperforms the traditional LDA methods significantly.