基于特征融合的层次结构微博情感分类

朱宪莹; 刘箴; 金炜; 刘婷婷; 刘翠娟; 柴艳杰

doi:10.11959/j.issn.1000-0801.2016182

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基于特征融合的层次结构微博情感分类

研究与开发 | 更新时间：2024-06-05

- 基于特征融合的层次结构微博情感分类
- Hierarchical micro-blog sentiment classification based on feature fusion
- 电信科学 2016年32卷第7期页码：106-114
- 作者机构：
- 作者简介：
  
  [ "朱宪莹（1991-），女，宁波大学信息科学与工程学院硕士生，主要研究方向为文本情感分析。" ]
  [ "刘箴（1965-），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为虚拟现实和社会媒体。" ]
  [ "金炜（1969-），男，博士，宁波大学信息科学与工程学院副教授，主要研究方向为图像处理。" ]
  [ "刘婷婷（1980-），女，宁波大学信息科学与工程学院博士生，主要研究方向为虚拟现实和社会媒体。" ]
  [ "刘翠娟（1979-），女，宁波大学信息科学与工程学院博士生，主要研究方向为社会媒体。" ]
  [ "柴艳杰（1968-），女，宁波大学讲师，主要研究方向为信息检索和动漫仿真。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61373068);国家自然科学基金资助项目;The National Natural Science Foundation of China(61271399);宁波市科技计划基金资助项目;Ningbo Science and Technology Plan Project(2015A610128);宁波市科技计划基金资助项目;Ningbo Science and Technology Plan Project(2015C50053);宁波市科技计划基金资助项目;Ningbo Science and Technology Plan Project(2015D10011);宁波市科技创新团队研究计划基金资助项目;Science and Technology Innovation Team Research Project of Ningbo(2011B81002);高等学校博士学科点专项科研基金资助项目;Specialized Research Fund for the Doctoral Program of Higher Education(20133305110004)
- DOI：10.11959/j.issn.1000-0801.2016182
  中图分类号： TP391
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-15
- 稿件说明：
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朱宪莹, 刘箴, 金炜, 等. 基于特征融合的层次结构微博情感分类[J]. 电信科学, 2016,32(7):106-114.

Xianying ZHU, Zhen LIU, Wei JIN, et al. Hierarchical micro-blog sentiment classification based on feature fusion[J]. Telecommunications science, 2016, 32(7): 106-114.
朱宪莹, 刘箴, 金炜, 等. 基于特征融合的层次结构微博情感分类[J]. 电信科学, 2016,32(7):106-114. DOI： 10.11959/j.issn.1000-0801.2016182.

Xianying ZHU, Zhen LIU, Wei JIN, et al. Hierarchical micro-blog sentiment classification based on feature fusion[J]. Telecommunications science, 2016, 32(7): 106-114. DOI： 10.11959/j.issn.1000-0801.2016182.

摘要

情感分类是观点挖掘的热点研究之一，微博文本情感分类具有很高的应用价值。鉴于传统特征选择方法存在语义缺陷，采用神经网络语言模型，提出了基于概率模型的对词向量进行权重分配的深层特征表示方法，构建文本语义向量。将文本深层特征与浅层特征融合，构建融合语义信息的特征向量，弥补传统特征选择方法语义的缺陷。采用SVM层次结构分类模型，实现多种情感分类。实验结果表明，采用特征融合的层次结构情感分类方法，能有效提高微博情感分类的准确率。

Abstract

Sentiment classification is an important issue of opinion mining.It has a high application value to classify sentiment in micro-blogs.As traditional feature selection method has semantic gap

a neural network language model was used to propose a deep feature representation method based on probability model to distribute weight to the word vector.Using this method

text semantic vector could be built.In order to avoid the semantic gap

the deep features and shallow features of text were integrated and feature vector that contained semantic information was constructed.With SVM hierarchical classification model

a variety of sentiments could be classified.Experimental results show that the hierarchical sentiment classification method based on feature fusion can improve the accuracy of sentiment classification in micro-blogs.

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Keywords

references

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