A Sentiment Feature Selection Method Based on Rough Set and Information Gain

PU Guo-lin

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A Sentiment Feature Selection Method Based on Rough Set and Information Gain

更新时间：2026-09-04

- A Sentiment Feature Selection Method Based on Rough Set and Information Gain
- Vol. 33, Issue 1, Pages: 96-99(2016)
- 作者机构：
  
  四川文理学院机构计算机学院,四川,达州,635000
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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蒲国林. 基于粗糙集与信息增益的情感特征选择方法[J]. 微电子学与计算机, 2016, 33(1): 96-99.

PU Guo-lin. A Sentiment Feature Selection Method Based on Rough Set and Information Gain[J]. 2016, 33(1): 96-99.
蒲国林. 基于粗糙集与信息增益的情感特征选择方法[J]. 微电子学与计算机, 2016, 33(1): 96-99. DOI：

PU Guo-lin. A Sentiment Feature Selection Method Based on Rough Set and Information Gain[J]. 2016, 33(1): 96-99. DOI：

摘要

为了提高情感特征提取的准确率

为高性能情感分析打下坚实的基础

提出了一种融合粗糙集与信息增益的情感特征选择方法.该方法借助信息增益判据选出高相关性的特征子集

再通过粗糙集剔除高冗余性的特征

从而得到最优的特征子集.在多个数据集上的测试表明

该方法可将若干经典方法的准确率提高4~9个百分点

是一种优秀的特征选择方法

对提升情感分析的整体性能有明显意义.

Abstract

A Rough Set and Information Gain based on sentiment feature selection method is proposed for building a solid foundation in sentiment analysis. The novel method firstly uses Information Gain to select a feature subset which has high relativity with the class attribute. Secondly

the features which have high redundancy will be eliminated by Rough Set. Experimental results on several datasets reveal the method makes accuracy increase 4-9 percentages than other methods. It is an outstanding feature selection method and has significance in sentiment analysis.

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references

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