基于多类别语义词簇的新闻读者情绪分类  

Emotion classification for news readers based on multi-category semantic word clusters

作　　者：温雯[1] 吴彪[1] 蔡瑞初[1] 郝志峰[1,2] 王丽娟[1] 

机构地区：[1]广东工业大学计算机学院,广州510006 [2]佛山科学技术学院数据与大数据学院,广东佛山528000

出　　处：《计算机应用》2016年第8期2076-2081,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61202269;61472089)~~

摘　　要：分析和研究文本读者情绪有助于发现互联网的负面信息,是舆情监控的重要组成部分。考虑到引起读者不同情绪主要因素在于文本的语义内容,如何抽取文本语义特征因此成为一个重要问题。针对这一问题,提出首先使用word2vec模型对文本进行初始的语义表达;在此基础上结合各个情绪类别分别构建有代表性的语义词簇,进而采用一定准则筛选对类别判断有效的词簇,从而将传统的文本词向量表达改进为语义词簇上的向量表达;最后使用多标签分类方法进行情绪标签的学习和分类。实验结果表明,该方法相对于现有的代表性方法来说能够获得更好的精度和稳定性。The analysis and study of readers＇ emotion is helpful to find negative infolanation of the Internet, and it is an important part of public opinion monitoring. Taking into account the main factors that lead to the different emotions of readers is the semantic content of the text, how to extract semantic features of the text has become an important issue. To solve this problem, the initial features related to the semantic content of the text was expressed by word2vec model. On the basis of that, representative semantic word clusters were established for all emotion categories. Furthermore, a strategy was adopted to select the representative word clusters that are helpful for emotion classification, thus the traditional text word vector was transformed to the vector on semantic word clusters. Finally, the multi-label classification was implemented for the emotion label learning and classification. Experimental results demonstrate that the proposed method achieves better accuracy and stability compared with state-of-the-art methods.

关 键 词：情感分析 情绪分类 语义词簇 多标签学习 word2vec 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]