收稿日期: 2015-09-17
修回日期: 2015-10-20
网络出版日期: 2015-11-05
基金资助
本文系国家自然科学基金面上项目"大数据环境下多媒体网络舆情信息的语义识别与危机响应研究" (项目编号:71473101)研究成果之一。
Analysis on Key Technologies of Semantic Recognition of Network Public Opinion
Received date: 2015-09-17
Revised date: 2015-10-20
Online published: 2015-11-05
黄微 , 张耀之 , 李瑞 . 网络舆情信息语义识别关键技术分析[J]. 图书情报工作, 2015 , 59(21) : 33 -37 . DOI: 10.13266/j.issn.0252-3116.2015.21.005
[Purpose/significance]This paper summarizes and analyzes the main key technologies of semantic recognition of network public opinion. It analyzes the present research situation on pretreatment technology of network public opinion information, analysis technology of emotional tendency and identification technology of network public opinion topics in detail. [Method/process]This paper proposes the processing accuracy, complexity, universality and adaptation of technology, and makes a comparative analysis on the key technologies of semantic recognition of network public opinion information based on these.[Result/conclusion]A review of the advantages and disadvantages of various technologies is made, in order to explore the future development direction of the technology and provide the reference for the effective monitor and management of public opinion.
[1] 闫乐林.基于视听信息的视频语义分析与检索技术研究[D]. 北京:北京邮电大学,2012. [2] 胡冬润. 面向网络舆情任务的本体学习方法研究[D].南京: 南京航空航天大学,2013.
[3] 王铁套,王国营,陈越,等.基于语义模式与词汇情感倾向的舆情态势研究[J].计算机工程与设计,2012,33(1):74-75.
[4] General inquirer[EB/OL]. [2015-09-14].http://www.wjh.harvard.edu/~Inquirer/.
[5] Fellbaum C. WordNet: An electronic lexical database[M].New Jersey:BradfordBook,1998.
[6] Esuli A. Sebastiani F. SentiwordNet: A publicly available lexical resource for opinion mining[C]//Proceedings of the 5th Conference on Language Resources and Evaluation. Genoa:European Language Resources Association,2006.
[7] HowNet. HowNet's homepage[OL].[2015-08-29] http://www.keenage.com
[8] 马海兵,刘永丹,王兰成.三种文档语义倾向性识别方法的分析与比较[J]. 现代图书情报技术,2007(4):43-46.
[9] Pang B, Lee L,Vaithynanthan S. Thumbs up sentiment classification using machine learning techniques[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Philadelphia:Association for Computational Linguistics. 2002: 79- 86.
[10] 王伟凝,余英林.图像的情感语义研究进展[J].电路与系统学报,2003,8(5):53-54.
[11] 相洁,陈俊杰, 董志锋.基于本体的图像情感语义识别及推理模型[J].计算机工程与设计,2008,29(6):1494-1496.
[12] 相洁, 陈俊杰.基于本体的图像情感语义识别研究[J].计算机工程与应用,2008,44(27):43-46.
[13] 王岚,王立鹏.基于OCC的Agent情感模型研究[J].微计算机信息,2007,23(5):256-258.
[14] 陆泉,丁恒.基于情感的图像检索研究综述[J].情报理论与实践,2013,36(2):119-121.
[15] 张晨逸,孙建伶,丁轶群.基于MB-LDA模型的微博主题挖掘[J].计算机研究与发展,2011,48(10):1795-1796.
[16] 谢昊,江红.一种面向微博主题挖掘的改进LDA模型[J].华东师范大学学报(自然科学版),2013,93(6):93-96.
[17] Blei D M,Lafferty J D.Dynamic topic models[C]//Proceedings of the 23rd International Conference on Machine Learning.New York:ACM,2006:113-120.
[18] 王强,关毅,王晓龙.基于标题类别语义识别的文本分类算法研究[J]. 电子与信息学报,2007,29(12):2885-2888.
[19] 朱晓峰, 陈楚楚,尹婵娟.基于微博舆情监测的K-Means算法改进研究[J].情报理论与实践,2014,37(1):136-138.
[20] 郭跇秀,吕学强,李卓.基于突发词聚类的微博突发事件检测方法[J]. 计算机应用,2014,34(2):486-488.
[21] 万源.基于语义统计分析的网络舆情挖掘技术研究[D].武汉:武汉理工大学,2012.
[22] 王功辉, 黄奇,秦超.本体构建中的语义分析方法研究[J].图书情报工作,2013,57(7):106-108.
[23] 戴媛,程学旗.面向网络舆情分析的实用关键技术概述[J].信息网络安全,2008,90(6):62-64.
/
| 〈 |
|
〉 |
