基于短文本情感分析的敏感信息识别

李扬; 潘泉; 杨涛

doi:10.7652/xjtuxb201609013

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基于短文本情感分析的敏感信息识别

更新时间：2025-07-09

- 基于短文本情感分析的敏感信息识别
- Sensitive Information Recognition Based on Short Text Sentiment Analysis
- 西安交通大学学报 2016年50卷第9期页码：80-84
- 作者机构：
  
  西北工业大学自动化学院,西安,710072
- 作者简介：
- 基金信息：
  
  : 2015-12-23。作者简介: 李扬(1990—),男,博士生;杨涛(通信作者),男,副教授。基金项目: 国家自然科学青年基金资助项目(61402373);中国博士后科学基金面上资助项目(2014M562419)
- DOI：10.7652/xjtuxb201609013
  中图分类号： TP271
- 网络首发：2016-09-10，
  
  纸质出版：2016
- 稿件说明：
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李扬, 潘泉, 杨涛. 基于短文本情感分析的敏感信息识别[J]. 西安交通大学学报, 2016,50(9):80-84.

Sensitive Information Recognition Based on Short Text Sentiment Analysis[J]. 2016, 50(9): 80-84.
李扬, 潘泉, 杨涛. 基于短文本情感分析的敏感信息识别[J]. 西安交通大学学报, 2016,50(9):80-84. DOI： 10.7652/xjtuxb201609013.

Sensitive Information Recognition Based on Short Text Sentiment Analysis[J]. 2016, 50(9): 80-84. DOI： 10.7652/xjtuxb201609013.

摘要

针对现有的敏感信息识别是基于敏感关键词匹配方式判断的

准确度不是很高

且具有较高的误检率等问题

提出了敏感关键词与情感极性协同分析的敏感信息识别方法。在真实数据集上

利用监督学习的方式对微博的情感极性进行了度量

得到具体的情感极度

并将文本分为正情感极性和负情感极性两类。通过定义色情、暴力、违禁、邪教、反动等5大类2 639个敏感关键词和在数据集里面所呈现的Zipf分布特性

发现含有负情感极性的微博内容具有较高的敏感性

由此深入研究了敏感关键词对情感极性的动力因素

构建了含有情感极性因素的敏感度模型

提出了敏感信息的判别方法

敏感信息检测的准确率由传统方法的31.25%提高到了58.75%

召回率则由95%提升到96%

综合指标F值从47.0%提升到了72.3%。

Abstract

The existing sensitive information recognition is based on the sensitive keyword matching method

so the accuracy is low and the miss rate is high. We presented a collaborative method by using the sensitive keywords and sentiment polarities to identify the sensitive information. In the real dataset

we used the supervised way to measure the sentiment polarities of the blogs

and divided the blogs into two categories

namely the blogs are with positive or negative sentiment polarities. Five kinds of 2 639 sensitive keywords

including pornography

violence

illegality

cult and reactionary

were defined

and it was found that according to the Zipf distribution of these words in the dataset

the contents of blogs with negative sentiment polarities exhibited high sensitivities. Then we studied the contribution of the sensitive keywords to the sentiment polarity

and constructed the model of sensitivity degree that contains the sentiment polarity factor. Based on this

we proposed a new way to identify the sensitive information

which makes the accuracy and miss rate improved from 31.25% to 58.75% and from 95% to 96%

respectively

and the F-measure was improved from 47.0%to 72.3%.

关键词

Keywords

references

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ZHOU Donghao, HAN Wenbao. Diffrank: a novel algorithm for information diffusion detection in social networks [J]. Chinese Journal of Computer, 2014, 37(4): 884-892.

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