短文本特征的组合加权方法

谭有新; 滕少华

doi:10.12052/gdutxb.200019

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短文本特征的组合加权方法

综合研究 | 更新时间：2026-05-27

- 短文本特征的组合加权方法
- Combined Weighting Method for Short Text Features
- 广东工业大学学报 2020年37卷第5期页码：51-61
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  谭有新(1995–)，男，硕士研究生，主要研究方向为机器学习、自然语言处理
  滕少华(1962–)，男，教授，博士，主要研究方向为大数据、角色分配、网络安全、协同计算、机器学习，E-mail：Shteng@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61972102);广东省科技计划项目(2016B010108007，2019B110210002，2019B020208001);广东省教育厅项目(粤教高函〔2018〕179号);广州市科技计划项目(201802010042，201802030011，201802010026，201903010107)
- DOI：10.12052/gdutxb.200019
  中图分类号： TP391
- 网络首发：2020-09-17，
  
  纸质出版：2020
- 稿件说明：
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谭有新, 滕少华. 短文本特征的组合加权方法[J]. 广东工业大学学报, 2020, 37(5): 51-61.

Tan You-xin, Teng Shao-hua. Combined Weighting Method for Short Text Features[J]. 2020, 37(5): 51-61.
谭有新, 滕少华. 短文本特征的组合加权方法[J]. 广东工业大学学报, 2020, 37(5): 51-61. DOI： 10.12052/gdutxb.200019.

Tan You-xin, Teng Shao-hua. Combined Weighting Method for Short Text Features[J]. 2020, 37(5): 51-61. DOI： 10.12052/gdutxb.200019.

摘要

文本情感分析是自然语言处理的典型任务

但是现有情感分析正确率不高

其中词的特征化是一个重要原因。本文提出了一种短文本特征的组合加权方法(a Combined Weighting method for Short Text Features，CWSTF)

可以有效提高情感分析正确率。CWSTF方法以随机森林为基础评估特征对于情感的贡献度并排序

进而依排序来进行特征选择。然后考虑特征在文档中的重要性TF-IDF(Term Frequency–Inverse Document Frequency)

以特征在文档中的重要性和情感贡献度确定该特征的权重。最后

用支持向量SVM(Support Vector Machine)、朴素贝叶斯NB(Naive Bayes)、最大熵ME(Maximum Entropy)、K最近邻KNN(K-NearestNeighbor)等分类器进行比较实验

实验结果表明采用本文方法处理的特征

比其余方法能有效提高情感分类正确率。

Abstract

Text sentiment analysis is a typical task of natural language processing

but the accuracy of existing sentiment analysis is not high

and word characterization is an important reason. A combined weighting method for short text features (CWSTF) is proposed

which can effectively improve the accuracy of sentiment analysis. The CWSTF method evaluates the contribution of features to emotions based on random forests and ranks them

and then filters features based on ranks. Then

the importance of the feature in the document is calculated by TF-IDF (Term Frequency–Inverse Document Frequency)

and the final weight of the feature is determined by the importance of the feature in the document and the contribution to the sentiment; Finally

four such classifiers as SVM (Support Vector Machine)

NB (Naive Bayes)

ME (Maximum Entropy)

and KNN (K-Nearest Neighbor) are used for comparison experiments. The experimental results show that the features processed by proposed method can more effectively improve the accuracy of sentiment classification than other methods.

关键词

Keywords

references

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