Sensitive Information Recognition Based on Short Text Sentiment Analysis

doi:10.7652/xjtuxb201609013

您当前的位置：

首页 >

文章列表页 >

Sensitive Information Recognition Based on Short Text Sentiment Analysis

更新时间：2025-07-09

- Sensitive Information Recognition Based on Short Text Sentiment Analysis
- Vol. 50, Issue 9, Pages: 80-84(2016)
- 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：
- DOI：10.7652/xjtuxb201609013
  CLC： TP271
- Online First：10 September 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
Sensitive Information Recognition Based on Short Text Sentiment Analysis[J]. 2016, 50(9): 80-84.
DOI：

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

WU K, ZHANG B, ZHENG J, et al. Sentiment classification for topical Chinese microblog based on sentences' relations [C]∥The IEEE International Conference on Cyber, Physical and Social Computing. Piscataway, NJ, USA: IEEE, 2013: 2221-2225.

ZHAO J, DONG L, WU J, et al. Moodlens: an emoticon-based sentiment analysis system for Chinese Tweets [C]∥Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2012: 1528-1531.

LIU B. Sentiment analysis and opinion mining [J]. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167.

WANG G, SUN J, MA J, et al. Sentiment classification: the contribution of ensemble learning [J]. Decision Support Systems, 2014, 57(1): 77-93.

张鲁民, 贾焰, 周斌. 一种基于情感符号的在线突发事件检测方法 [J]. 计算机学报, 2013, 36(8): 1659-1667.

ZHANG Lumin, JIA Yan, ZHOU Bin. Online bursty events detection based on emoticons [J]. Chinese Journal of Computers, 2013, 36(8): 1659-1667.

CAO J, ZENG K, WANG H, et al. Web-based traffic sentiment analysis: methods and applications [J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 844-853.

SOCHER R, PERELYGIN A, WU J, et al. Recursive deep models for semantic compositionality over a sentiment treebank [C]∥Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, Washington, USA: Association for Computational Linguistics, 2013: 1631-1642.

SANTOS C, GATTIT M. Deep convolutional neural networks for sentiment analysis of short texts [C]∥Proceedings of the 25th International Conference on Computational Linguistics. Dublin, Ireland: Coling, 2014: 69-72.

LAI S, XU L, LIU K, et al. Recurrent convolutional neural networks for text classification [C]∥Proceedings of the 29th AAAI Conference on Artificial Intelligence. Menlo Park, California, USA: AAAI, 2015: 2267-2273.

ZHOU C, SUN C, LIU Z, et al. A C-LSTM neural network for text classification [EB/OL]. [2016-02-20]. http: ∥arxiv. org/abs/1511.08630.

WANG P, XU B, XU J, et al. Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification [J]. Neurocomputing, 2015, 174: 806-814.

ZHANG X, ZHAO J, LECUN Y. Character-level convolutional networks for text classification [EB/OL]. [2016-02-20]. http: ∥arxiv. org/abs/1509. 01626.

WANG X, ZHU F, JIANG J, et al. Real time event detection in Twitter [J]. Lecture Notes in Computer Science, 2013, 7923: 502-513.

WENG J S, YAO Y X, LEONARDI E, et al. Event detection in Twitter [C]∥Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Menlo Park, California, USA: AAAI, 2011: 401-408.

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.

BOLLEN J, PEPE A, MAO H. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena [C]∥Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media(ICWSM 2011). Menlo Park, California, USA: AAAI, 2011: 450-453.

CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273-297.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

MCTE: a Trust Evaluation Model for Multiple Context in Social Networks

Mechanism and Optimal Control Strategy for Hysteresis in Rapid Dehumidification Response of Indirect Thermal Systems in Electric Vehicles

A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction

I/O Performance Modeling and Prediction Method for Large-Scale Parallel Applications Using Machine Learning

Effects of Steam Addition on the Swirl Combustion and Emission Characteristics of Cracked Ammonia

Related Author

孟宪佳

马建峰

王一川

卢笛

Related Institution

School of Computer Science and Technology, Xidian University

⁰