基于补全矩阵的多标签相关性情感分类①.PDF

2017 年 第 26 卷 第 1 期 计 算 机 系 统 应 用 基于补全矩阵的多标签相关性情感分类① 许莉莉, 高俊波, 李胜宇 (上海海事大学 信息工程学院, 上海 201306) 摘 要: 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%. 关键词: 社会新闻; 情感分析; 标签相关性; 补全矩阵-LDA(CM-LDA); 多标签分类 Emotion Classification of Multi-Label Correlation Based on Completion Matrix XU Li-Li, GAO Jun-Bo, LI Sheng-Yu (College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China) Abstract: 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. Key words: social news; sentiment analysis; label correlation; completion matrix-LDA(CM-LDA); multi-label classification 1 引言 后读者的投票信息作为样本类别标注的先验知识, 提 目前, 从作者角度分析, 有关新闻文本的情感分 出了一种半监督学习的分类模型,