基于语义统计分析的网络舆情挖掘技术研究
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摘要
随着互联网技术的发展和网络应用的普及,互联网成为民众获取信息的重要来源,同时也成为人们传播信息和表达观点的重要渠道。通过网络了解社情民意,关注舆情动向,对于促进社会和谐稳定、推动社会民主与法制建设具有重要的现实意义。
网络信息浩如烟海,鱼龙混杂,人工识别和研判显得力不从心。如何利用计算机网络技术、人工智能技术和数据挖掘技术,对网络舆情信息有效地挖掘和分析成为了一个新的研究热点。如何识别民众所关注的热点话题并有效地分类,如何判断民众对社会事件的态度是正向的还是反向的,如何分析和把握社会热点事件的波动性等,是网络舆情研究中的亟需解决的重点问题,对认识和引导网络舆情具有重要的科学意义。
本文针对网络舆情信息的挖掘和分析中存在的问题展开研究,将基于Web的文本分类技术、机器学习算法研究、波动性的统计分析等技术应用到网络突发事件的分类、网络舆情信息的情感倾向性分析、舆情演变的波动性分析等研究中。主要的研究内容和创新点包括：
1.本文提出将Fisher判别准则应用到网络舆情文本的分类问题中,并实现对突发事件的分类。由突发事件引发的网络舆情信息,从内容形式来看主要为文本,因此舆情信息的分类本质上是文本分类的问题。Fisher判别准则是解决降维问题的有效方法之一,但在文本分类中研究较少。本文将Fisher判别准则作为分类的特征提取的方法应用于文本分类,并应用于网络突发事件新闻报道的分类。针对舆情的相关研究,将突发事件按照公共安全的四类,即突发自然灾害、事故灾难、公共卫生事件和社会安全事件进行分类。实验证明,Fisher准则的方法略逊于信息增益,但比较其他特征选择方法都更好。
2.本文提出基于类别相关度的局部潜在语义分析的算法LR-LSA,改进了局部潜在语义分析算法进行文本分类。分析介绍了潜在语义分析的原理,包括奇异值分解和计算文档间相似关系的方法。通过分析潜在语义分析方法的局限性,提出算法LR-LSA,先利用SVM分类器给每篇文档一个类别相关度,然后根据相关度的大小选择生成局部区域的方法。在中文Web文本语料的两组分类实验中,验证了算法LR-LSA比LSA和LC-LSA更有效。
3.针对机器学习方法在情感倾向性分析中对情感语义信息考虑不足的问题,本文提出了将情感模式和机器学习相结合的方法PMML,并应用于Web评论文本的情感倾向性分类。介绍了情感倾向性分析的相关研究,包括不同粒度级的基于情感词典分析的分类方法,以及基于机器学习的情感倾向性分类方法。利用本文提出的方法PMML对Web评论文本分类,在对原始语料文本进行基本的分词之后,先进行关键词提取,再进行模式匹配来提取情感评价短语,匹配成功后形成相应的情感特征序列,分别计算每个特征的情感倾向值,再通过机器学习的方法最终得到文本的情感倾向。实验验证了PMML方法较之机器学习的方法在分类效果上的有效性。
4.本文提出基于GARCH类模型的网络舆情信息演变的波动性研究方法。在热点事件的传播过程中,波动性是其重要特征之一,强烈的波动往往意味着信息内容的不断传播和各种流言的不断蔓延,存在转变为突发事件的可能性。通过分析网络舆情演变过程中的波动性的特点,如变化率序列呈现的异方差性、尖锋厚尾性等,对比金融领域的波动性,本文提出基于GARCH类模型的波动性研究方法。通过热点事件在主流搜索引擎中采集到的Web页面数,定量地分析与这个事件相关的舆情演变趋势。选择社会热点事件“温州动车事故”,通过收集数据,分析变化率,分别建立了GARCH, EGARCH, TARCH模型。实证分析了说明GARCH类模型对舆情演变的波动性分析的可行性。
With the development of Internet technology and applications of network, the Internet has become an important source, even the main source from which public access to information. It also becomes an important place for people to exchange information and to express their views. Understanding Internet public opinion through the net and concerning about trends of Internet public opinion have important practical significance for the maintenance of social harmony and stability and also for the promotion of social democracy and legal construction.
Information on the Internet is so enormous that the identification and assessment in manual way are powerless. How to use computer network technology, artificial intelligence techniques and data mining techniques to mine and analyze the Internet public opinion becomes a new research focus. There are some urgent and important issues in this domain, such as how to identify and categorize the hot topics, from public opinion information on web; how to determine whether the attitude of the people on an social event is positive or negative; how to analyze trend of the fluctuation of the hot social events, etc. These issues have important scientific and practical significance on recognizing and guide of Internet public opinion.
This dissertation does some research in the mining and analysis of Internet public opinion information, such as utilizing Web document classification technique to do some classification of emergencies on Web, adopting machine learning methods to analyze the sentimental orientation of Internet public opinion, and giving statistic analysis for fluctuation in investigating the trend of Internet public opinion. Specific contents and the innovations are as follows:
1. In this dissertation, Fisher discriminate analysis is introduced in text classification of Internet public opinion, and then so is the classification of emergencies. Internet public opinion, caused by the unexpected events, is in the content form of document, classification of Internet public opinion is then converted to one text classification problem. Fisher criterion is an effective way in solving the dimensionality reduction problem, but few studies are available in text classification. As a feature extraction method, Fisher discriminate criterion is applied for text classification problems, and then is used in classification of emergency management. As for Internet public opinion research, according to accordance public safety, emergencies are always classified into four types of emergencies, sudden natural disasters, accidents, disasters, public health events, social security events. The experiments proved that the Fisher criteria slightly inferior to the method of information gain, but compared with other feature extraction methods are better.
2. Through analyzing of latent semantic analysis theory, including the singular value decomposition, calculation of the similarity relations between documents, this dissertation proposed a new algorithm, LR-LSA, for web texts classification. In order to eliminate the analysis of the limitations of the method of latent semantic analysis, the proposed algorithm LR-LSA, SVM classifier for a category relevance of each document, and then uses the correlation to different categories to generate the local area. Two classification experiments on Chinese corpus verified that the performance of the LR-LSA is more effective than LSA.
3. The method based on machine learning for the sentiment analysis considers less sentimental feature extraction, and then in this dissertation we present a method, PMML, combining the feature extraction and machine learning for analysis of sentiment. We introduced relevant research, including the classification at the different granularity, and the methods based on machine learning. Web comments texts, after conducting basic sub-word first, are divided into set of keywords. For those key words, we designed some patterns which are often used in emotional expression. After the success matching to those patterns, emotional features are gotten and in the form of sequence. For each feature pattern, we calculated separately the emotional tendency, and then adopting machine learning method finally to obtain the emotional tendencies of the web comment. The experiments illustrate the effectiveness of the PMML when compared to machine learning method in the classification performances.
4. This dissertation analyzes the evolution of fluctuations of Internet public opinion information based on GARCH model. In the propagation of hot events, fluctuation is an important feature of the strong fluctuations often means that the information content are copied, reproduced and update fast. By the analysis of the evolution of Internet public opinion in the fluctuations of characteristics, such as the rate of change of sequence showing the heteroscedasticity with sharp peak and thick tail, and compared to the fluctuation of the financial variables, we introduce GARCH models to express such fluctuations. Use the numbers of Web pages that are collected by the hot events from the major search engines and quantitative analysis of public opinion, the evolution of trends are investigated associated with this event. We choose social hot events "Wenzhou Train Accident" as example, including data collection, calculation of the rate of change and we establish GARCH, EGARCH, TGARCH models, respectively. The empirical analysis shows that these GARCH models are feasible in the analysis of the fluctuation of public opinion evolution.
网络信息浩如烟海,鱼龙混杂,人工识别和研判显得力不从心。如何利用计算机网络技术、人工智能技术和数据挖掘技术,对网络舆情信息有效地挖掘和分析成为了一个新的研究热点。如何识别民众所关注的热点话题并有效地分类,如何判断民众对社会事件的态度是正向的还是反向的,如何分析和把握社会热点事件的波动性等,是网络舆情研究中的亟需解决的重点问题,对认识和引导网络舆情具有重要的科学意义。
本文针对网络舆情信息的挖掘和分析中存在的问题展开研究,将基于Web的文本分类技术、机器学习算法研究、波动性的统计分析等技术应用到网络突发事件的分类、网络舆情信息的情感倾向性分析、舆情演变的波动性分析等研究中。主要的研究内容和创新点包括：
1.本文提出将Fisher判别准则应用到网络舆情文本的分类问题中,并实现对突发事件的分类。由突发事件引发的网络舆情信息,从内容形式来看主要为文本,因此舆情信息的分类本质上是文本分类的问题。Fisher判别准则是解决降维问题的有效方法之一,但在文本分类中研究较少。本文将Fisher判别准则作为分类的特征提取的方法应用于文本分类,并应用于网络突发事件新闻报道的分类。针对舆情的相关研究,将突发事件按照公共安全的四类,即突发自然灾害、事故灾难、公共卫生事件和社会安全事件进行分类。实验证明,Fisher准则的方法略逊于信息增益,但比较其他特征选择方法都更好。
2.本文提出基于类别相关度的局部潜在语义分析的算法LR-LSA,改进了局部潜在语义分析算法进行文本分类。分析介绍了潜在语义分析的原理,包括奇异值分解和计算文档间相似关系的方法。通过分析潜在语义分析方法的局限性,提出算法LR-LSA,先利用SVM分类器给每篇文档一个类别相关度,然后根据相关度的大小选择生成局部区域的方法。在中文Web文本语料的两组分类实验中,验证了算法LR-LSA比LSA和LC-LSA更有效。
3.针对机器学习方法在情感倾向性分析中对情感语义信息考虑不足的问题,本文提出了将情感模式和机器学习相结合的方法PMML,并应用于Web评论文本的情感倾向性分类。介绍了情感倾向性分析的相关研究,包括不同粒度级的基于情感词典分析的分类方法,以及基于机器学习的情感倾向性分类方法。利用本文提出的方法PMML对Web评论文本分类,在对原始语料文本进行基本的分词之后,先进行关键词提取,再进行模式匹配来提取情感评价短语,匹配成功后形成相应的情感特征序列,分别计算每个特征的情感倾向值,再通过机器学习的方法最终得到文本的情感倾向。实验验证了PMML方法较之机器学习的方法在分类效果上的有效性。
4.本文提出基于GARCH类模型的网络舆情信息演变的波动性研究方法。在热点事件的传播过程中,波动性是其重要特征之一,强烈的波动往往意味着信息内容的不断传播和各种流言的不断蔓延,存在转变为突发事件的可能性。通过分析网络舆情演变过程中的波动性的特点,如变化率序列呈现的异方差性、尖锋厚尾性等,对比金融领域的波动性,本文提出基于GARCH类模型的波动性研究方法。通过热点事件在主流搜索引擎中采集到的Web页面数,定量地分析与这个事件相关的舆情演变趋势。选择社会热点事件“温州动车事故”,通过收集数据,分析变化率,分别建立了GARCH, EGARCH, TARCH模型。实证分析了说明GARCH类模型对舆情演变的波动性分析的可行性。
With the development of Internet technology and applications of network, the Internet has become an important source, even the main source from which public access to information. It also becomes an important place for people to exchange information and to express their views. Understanding Internet public opinion through the net and concerning about trends of Internet public opinion have important practical significance for the maintenance of social harmony and stability and also for the promotion of social democracy and legal construction.
Information on the Internet is so enormous that the identification and assessment in manual way are powerless. How to use computer network technology, artificial intelligence techniques and data mining techniques to mine and analyze the Internet public opinion becomes a new research focus. There are some urgent and important issues in this domain, such as how to identify and categorize the hot topics, from public opinion information on web; how to determine whether the attitude of the people on an social event is positive or negative; how to analyze trend of the fluctuation of the hot social events, etc. These issues have important scientific and practical significance on recognizing and guide of Internet public opinion.
This dissertation does some research in the mining and analysis of Internet public opinion information, such as utilizing Web document classification technique to do some classification of emergencies on Web, adopting machine learning methods to analyze the sentimental orientation of Internet public opinion, and giving statistic analysis for fluctuation in investigating the trend of Internet public opinion. Specific contents and the innovations are as follows:
1. In this dissertation, Fisher discriminate analysis is introduced in text classification of Internet public opinion, and then so is the classification of emergencies. Internet public opinion, caused by the unexpected events, is in the content form of document, classification of Internet public opinion is then converted to one text classification problem. Fisher criterion is an effective way in solving the dimensionality reduction problem, but few studies are available in text classification. As a feature extraction method, Fisher discriminate criterion is applied for text classification problems, and then is used in classification of emergency management. As for Internet public opinion research, according to accordance public safety, emergencies are always classified into four types of emergencies, sudden natural disasters, accidents, disasters, public health events, social security events. The experiments proved that the Fisher criteria slightly inferior to the method of information gain, but compared with other feature extraction methods are better.
2. Through analyzing of latent semantic analysis theory, including the singular value decomposition, calculation of the similarity relations between documents, this dissertation proposed a new algorithm, LR-LSA, for web texts classification. In order to eliminate the analysis of the limitations of the method of latent semantic analysis, the proposed algorithm LR-LSA, SVM classifier for a category relevance of each document, and then uses the correlation to different categories to generate the local area. Two classification experiments on Chinese corpus verified that the performance of the LR-LSA is more effective than LSA.
3. The method based on machine learning for the sentiment analysis considers less sentimental feature extraction, and then in this dissertation we present a method, PMML, combining the feature extraction and machine learning for analysis of sentiment. We introduced relevant research, including the classification at the different granularity, and the methods based on machine learning. Web comments texts, after conducting basic sub-word first, are divided into set of keywords. For those key words, we designed some patterns which are often used in emotional expression. After the success matching to those patterns, emotional features are gotten and in the form of sequence. For each feature pattern, we calculated separately the emotional tendency, and then adopting machine learning method finally to obtain the emotional tendencies of the web comment. The experiments illustrate the effectiveness of the PMML when compared to machine learning method in the classification performances.
4. This dissertation analyzes the evolution of fluctuations of Internet public opinion information based on GARCH model. In the propagation of hot events, fluctuation is an important feature of the strong fluctuations often means that the information content are copied, reproduced and update fast. By the analysis of the evolution of Internet public opinion in the fluctuations of characteristics, such as the rate of change of sequence showing the heteroscedasticity with sharp peak and thick tail, and compared to the fluctuation of the financial variables, we introduce GARCH models to express such fluctuations. Use the numbers of Web pages that are collected by the hot events from the major search engines and quantitative analysis of public opinion, the evolution of trends are investigated associated with this event. We choose social hot events "Wenzhou Train Accident" as example, including data collection, calculation of the rate of change and we establish GARCH, EGARCH, TGARCH models, respectively. The empirical analysis shows that these GARCH models are feasible in the analysis of the fluctuation of public opinion evolution.
引文
[1]陈力丹.舆论学-舆论导向研究[M].北京：中国广播电视出版社,1999：10-11.
[2]徐晓日.网络舆情事件的应急处理研究[J].华中电力大学学报(社会科学版),2007(1)：89-93.
[3]刘毅.网络舆情研究概论[M].天津人民出版社,2007.
[4]中国互联网络发展状况统计报告,中国互联网络信息中心,http://www.cnnic.cn/research/
[5]下载地址:http://www.iricn.com/index.php?option=com content&view=article&id= 258&Itemid=7
[6]曾润喜.网络舆情突发事件预警系统、指标与机制[J].情报杂志,2009,11：52-54.
[7]Yang,Y., Pedersen,J. A comparative Study on Feature Selection in Text Categorization[C]// ICML 1997,412-420.
[8]Liu,H.,Motoda,H. Feature Extraction, Construction and Selection:A Data Mining Perspective. Kluwer Academic, Norwell, MA, USA,1998.
[9]V. Vapnik, Statistical learning theory [M]. Wiley,1998.
[10]Joachims,T. Text categorization with support vector machines:learning with many relevant features.[C]//Proceedings of ECML-98,10th European Conference on Machine Learning, 137-142,1998.Springer Verlag.
[11]S.C.Deewester, S.T. Dumais, T.K. Landauer. Indexing by latent semantic analysis[J]. Journal of the American Society of Information Science,1990,41(6):391-407.
[12]Susan T. Dumais. Latent semantic indexing (lsi) and Trec-2[J]. NIST SPECIAL PUBLI-CATION SP,1994-trec.nist.gov.
[13]Kontostathis,A., Pottenger,W. A framework for understanding Latent Semantic Indexing (LSI) performance [J]. Information Processing and Management,2006,42(1):56-73.
[14]Picard RW. Affective computing [M]. MIT Press, Cambridge, MA,1997.
[15]Sentiment analysis [EB/OL].http://en.wikiPedia.org/wiki/Sentiment_analysis.
[16]赵军,许洪波,黄萱菁等.中文倾向性分析评测技术报告[C]//第一届中文倾向性分析评测研讨会,2008,1-20.
[17]Sznajd-Weron K., Sznajd J. Opinion Evolution in Closed Community [J].International Journal of Modern Physics,2000,11 (6):1157-1165.
[18]邱均平.网络信息计量学[M].武汉大学出版社出版2007.1
[19]Jon Kleinberg. Bursty and Hierarchical Structure in Streams[J]. Data Mining and Knowledge Discovery,2007,7(4):373-397.
[20]刘毅.内容分析法在网络舆情信息分析中的应用[J].天津大学学报：社会科学版,2006(7)：307-310.
[21]下载地址：http://www.iricn.com/index.php?option=com content&view=article&id=215& Itemid=7
[22]Brin S, L Page. The Anatomy of a Large-Scale HyPertextual WebSearch Engine[J]. Computer Networks and ISDN Systems,1998,30(1):107-117.
[23]Heydon A, M Najork. Mercator:A Scalable, Extensible Web Crawler[J]. World Wide Web, 1999,2(4):219-229
[24]刘金红,陆余良.主题网络爬虫研究综述[J].计算机应用研究,2007,24(10)：26-30
[25]Chakrabarti.S, Martin,V., B Dom. Focused Crawling:A New Approach to Topic-specific Web Resource Discovery [J]. Computer Networks,1999,31(11):1623-1640.
[26]J Allan, J Carbonell, G Doddington, et al. Topic detection andtracking pilot study:Final report[C]//Proceedings of the DARPA Broadcast NewsTranscription and Understanding Workshop, Virginia:Lansdowne,February 1998,194-218.
[27]J Allan, R Papka, V Lavrenko.On-line New Event Detection and Tracking[C]//Proceedings of SIGIR'98.University of Massachusetts:Amherst,1998,37-45.
[28]YB Liu, Cai, JR, J Yin, AWC Fu. Clustering Text Data Streams[J]. Journal of Computer Science and Technology,2008,23(1):112-128.
[29]XY Li, WB Croft. Improving novelty detection for general topics using sentence level information patterns[C]//Proceedings of the 15th ACM international conference on Information and knowledge management, Arlington, Virginia,2006:238-247
[30]RT Fernandez, DE Losada. Novelty detection using local context analysis[C]//Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, The Netherlands,2007:725-726.
[31]王允.网络舆情数据获取与话题分析技术研究[D]郑州,解放军信息工程大学,2010.6
[32]刘玉国.基于内容的互联网舆情信息挖掘关键技术研究[D]济南,山东大学,2011.4
[33]Y Yang, T Pierce, J Carbonell. A study on Retrospective and On-Line Event detection[C]// Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval.1998, CMU, USA:ACM,28-36.
[34]R Papka. On-line New Event Detection, Clustering and Tracking. Amherst:Department of Computer Science, UMASS,1999.
[35]J Allan, R Papka, V Lavrenko.On-Line New Event Detection and Tracking[C]//Proceedings of SIGIR'98:21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM Press,1998,37-45.
[36]Y Yang, T Pierce, J Carbonell. A study on Retrospective and On-Line Event detection[C]// Proceedings of the 21st international ACM SIGIR conference on Research and development in information retrieval.1998,CMU, USA:ACM,28-36.
[37]Y Zhang, JG Carbonell, J Allan. Topic Detection and Tracking:Detection-Task[C]// Proceedings of the Workshop of Topic Detection and Tracking,1997.
[38]J Carbonell, Y Yang, J Lafferty, et al. CMU Report on TDT-2:Segmentation, Detection and Tracking[C]//Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. SanFrancisco:Morgan Kauffman,1999,117-120.
[39]JM Schultz and M Liberman. Topic detection and tracking using idf-weighted cosine coefficient[C]//Proceedings of the DARPA Broadcast News Workshop. San Francisco: Morgan Kaufmann,1999,189-192.
[40]郭岩,白硕,于满泉.Web使用信息挖掘综述[J].计算机科学,2005,32(1)：1-7.
[41]General Inquirer [EB/OL]. http://www.wjh.harvard.edu/-Inquirer/
[42]Fellbaum C. WordNet:An Electronic Lexical Database[M].BradfordBook.1998.
[43]Esuli A. Sebastiani F. SentiwordNet:A Publicly available lexical resource for Opinion mining[C]//In Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation. Genova:IT2006.
[44]HowNetIM]. HowNet's HomePage. http://www.keenage.com
[45]梅家驹,竺一鸣,高蕴琦等.同义词辞林[M].第二版,上海：上海辞书出版社,1996.
[46]姚天防,娄德成.汉语情感词语义倾向判别的研究[C]//第七届中文信息处理国际会议,2007.
[47]柳位平,朱艳辉等.中文基础情感词词典构建方法研究[J].计算机应用,2009,29(10)：2875-2877.
[48]杜伟夫文本倾向性分析中的情感词典构建技术研究[D].哈尔滨,哈尔滨工业大学,2010.4.
[49]吴琼,谭松波,张刚等.跨领域倾向性分析相关技术研究[J].中文信息学报,2010,24(1)：77-84.
[50]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:2002:79-86.
[51]MULLEN T, COLL IER N. Sentiment analysis using support vector machiness with diverse information sources[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Barcelona:NLP,2004:412-418.
[52]J.Wiebe, E.Riloff. Creating subjective and objective sentence classifiers from unannotated text[C]//The 6th International Conference on computational Linguistics and Intelligent Text Processing,2005:486-497.
[53]唐慧丰,谭松波,程学旗.基于监督学习的中文情感分类技术比较研究中文信息学报,2007.11.
[54]徐军,丁宇新,王晓龙.使用机器学习方法进行新闻的情感自动分类[J].中文信息学报,2007,21(6)：95-100.
[55]罗亚平,王机,周延泉.基于关注度的热点话题发现模型[C]//In Proceeding of ICCC2007 workshop.402-408.
[56]李思,张浩,徐蔚然.基于合并模型的中文文本情感分析[C]//《第五届全国信息检索学术会议论文集》,2009：322-328.
[57]胡熠,陆汝占,李学宁等.基于语言建模的文本情感分类研究[J].计算机研究与发展,2007,44(9)：1469-1475.
[58]孙玉芬,卢炎生.流数据挖掘综述[J].计算机科学,2007,34(1)：1-5.
[59]袁志坚,王乐,田李等.数据流突发检测研究与进展[J].计算机工程与应用,2008,44(21)：1-4
[60]Toshiaki F., Tomoyuki N., Yasuhiro S. Identification of Bursts in a Document Stream[C]// The first International Workshop on Knowledge Discovery in Data Streams, Pisa,Italy,2004
[61]中华人民共和国突发事件应对法[EB/OL].http://www.gov.cn/ziliao/flfg/2007-08/30/ content 732593. htm.
[62]袁辛奋,胡子林.浅析突发事件的特征、分类和意义[J].科技与管理,2005.2 23-25
[63]He H, Chen B, Du L, et al. PRIS in TREC 2008 Blog Track[C]//TREC 2008.
[64]G.Salton, A.Wong C.S.Yang. A vector space model for automatic indexing[C]//Communica-tions of ACM 1975 (18),613-620,1975
[65]Salton G. McGill,M. Introduction to Modern Information Retrieval[M].McGraw-Hill.1983
[66]Yang,Y, Pedersen J.O. A comparative study on feature selection in text categorization. The 14th International Conference on Machine Learning,1997:412-420.
[67]Mladenic D, Grobelnik M. Feature selection for classification based on text hierarchy. Workshop on Learning from Text and Web.1998.
[68]Church K.W, Hanks P. Words association norms, mutual information and lexicography [J]. Computational Linguistics,1989,16(1):22-29.
[69]Dunning T. E. Accurate methods for the statistics of surprise and coincidence[J]. Computational Linguistics,1993,19(1):61-74.
[70]Ruizetal M. E. Automatic text categorization using neural networks[C]//The 8th ASIS SIG/ CR Classification Research,1997,8:59-72.
[71]Nigam K, Lafferty J, McCallum A. Using maximum entropy for text classification[C]//IJCAI Workshop on Machine Learning for Information Filtering,1999:61-67.
[72]Tokunaga T, Iwayama M. Text categorization based on weighted inverse document frequency [C]//SIG-IPS Japan,1994,100(5)
[73]Yang Y, Pedersen J.O. A comparative study on feature selection in text categorization[C]// The 14th International Conference on Machine Learning,1997:412-420.
[74]George Forman. An extensive empirical study of feature selection metrics for text classification [J].The Journal of Machine Learning Research archive,2003,3.
[75]Rogati M, Yang Y. High-performing feature selection for text classification[C]//CIKM'02, 2002:659-661.
[76]谭松波.高性能文本分类算法研究[D].中国科学院研究生院2006.
[77]单丽莉,刘秉权,孙承杰.文本分类中特征选择方法的比较与改进[J].哈尔滨工业大学学报2011,43(3)：321-326.
[78]Yan g M H. Kernel eigenfaces vs. kernel Fisherfaces:Facer ecognition using kernel methods [C]//Proceedin gs of the 5th IEEE Internat ional Conference on Automatic Face and Gesture Recognition. Was hington, DC, IEEE Computer Society,2002:215-220.
[79]Lu J W, Plataniot is K N, Venet sanopoulos A N. Face recognition using kernel direct discriminant analysis algorithms[J].IEEE Transactions on Neural Networks,2003,14(1): 117-126.
[80]吴成富,马松辉,陈怀民.地形匹配可信度分析[J].西北工业大学学报,2004,22(3)：346-349.
[81]Xiong T, Ye J P, Li Q, et al. Efficient kernel discriminant analysis via QR decomposition// Advances in Neural Information Processing Systems 17,2005.
[82]谭松波中文语料TanCorpV1.0,下载地址:http://www.searchforum.org.cn/tansongbo /corpus.htm.
[83]杜阿宁.互联网舆情信息挖掘方法研究[D]哈尔滨,哈尔滨工业大学,2007.
[84]赵旭东.互联网舆情指数挖掘方法[D]哈尔滨,哈尔滨工业大学,2007.
[85]Deerwester, S., Dumais, S., Landauer, T. et.al. Indexing by latent semantic analysis[J]. Journal of the Society for Information Science,41(6):391-407,1990.
[86]Dumais S. Using LSA for information filtering:TREC-3 experiments[C]//The Third Text Retrieval Conference (TREC-3). National Institute of Standards and Technology Special Publication,219-230.
[87]Landauer,T.,Foltz,P.,Laham,D. Introduction to latent semantic analysis [J]. Discourse Proce-sses,1998,25,259-284.
[88]Rosenstein M., Lochbaum C., Recommending from content:preliminary results from an e-commerce experiment[C]//Proceedings of Conference on Human Factors in Computing, 2000.
[89]Zelikovitz S., Hirsh H. Using LSI for Text Classification in the Presence of Background Text[C]//Proceedings of CIKM01,113-118,2001.
[90]Kitajima, M., Kariya, N., Takagi, H., & Zhang, Y. (2005). Evaluation of website usability using Markov chains and latent semantic analysis. IEICE Transactions on Communications, E88B(4),1467-1475.
[91]王永智,藤至阳,王鹏等.基于LSA和SVM的文本分类模型的研究[J].计算机工程与设计,2009,30(3)：729-734.
[92]王国勇,许建锁.一种基于LSA和Kohonen网络的文本分类新方法[J].计算机应用,2004,24(2)：53-56.
[93]Bo Yu, Zong-ben Xu, Cheng-hua Li. Latent semantic analysis for text categorization using neural network [J], Knowledge-Based Systems,21 (2008) 900-904.
[94]McCarthy P., Briner S., Rus V. Textual Signatures:identifying text-types using Latent Semantic Analysis to measure the cohesion of text structures[J].Natural Language Processing and Text Mining, Springer-Verlag, London,2006:107-122.
[95]张玉峰,何超.基于潜在语义分析和HS-SVM的文本分类模型研究[J].情报理论与实践,2010,33(7)：104-107.
[96]刘昌钰,唐常杰,于中华等.基于潜在语义分析的BBS文档Bayes鉴别器[J].计算机学报,2004,27(4)：566-572.
[97]潘渊,李弼程,张先飞LS-SVM：一种有效的新闻主题追踪方法[J].计算机应用研究,2008,25(9)：2661-2663.
[98]Hull, D, Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing// Proceedings of SIGIR'94, pp.282-289,1994.
[99]LIU T, CHEN Z, ZHANG BY, et al. Improving Text Classification using Local Latent Semantic Indexing[A]. Proceedings of the 4th IEEE International Conference on Data Mining [C]//2004.162-169.
[100]余正涛,樊孝忠,郭剑毅等.基于潜在语义分析的汉语问答系统答案提取[J].计算机学报,2006,29(10)：1889-1893.
[101]Schutze, H., Silverstein, C. Projections for Efficient Document Clustering[C]//Proceeding of SIGIR'97,1997,74-81.
[102]Wu, H., Gunopulos D. Evaluating the Utility of Statistical Phrases and Latent Semantic Indexing for Text Classification[C]//Proc. of ICDM'02,713-716,2002.
[103]Hatzivassiloglou V., McKeown K R. Predicting the semantic orientation of adjectives[C] //Proceedings of EACL. Morristown:ACL,1997.174-181.
[104]P.D.Turney. Thumbs up Or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews[C]//Proceedings of 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia:ACL,2002:417-424.
[105]梅家驹,竺一鸣,高蕴琦等.同义词辞林[M].第二版,上海：上海辞书出版社,1996.
[106]朱嫣岚,闵锦,周雅倩等.基于hownet的词汇语义倾向计算[J].中文信息学报,2006,20(1)：14-20.
[107]Jiang M, Xiao Sb, Wang Hw, et al. An improved word similarity computing method based on Hownet[J]. Journal of Chinese Information Processing,2008,22 (5):84289.
[108]徐琳宏,林鸿飞,杨志豪.基于语义理解的文本倾向性识别机制[J].中文信息学报,2007,21(1)：96-100.
[109]路斌,万小军,杨建武等.基于同义词词林的词汇褒贬计算[C]//第七届中文信息处理国 际会议论文集.2007：17-23.
[110]Weifu Du, Songbo Tan. Optimizing Modularity to Identify the Semantic Orientation of Chinese Words[J]. Expert Systems with Applications.2010,37(7):5094-5100.
[111]Wiebe, J. Learning subjective adjectives from corpora[C]//InProceeding of the 17th National Conference on Artificial Intelligence,Calif, AAAI Press,2000:735-740.
[112]Wiebe,J., WilsonT., BrueeR.et.al. Learning subjective language[J].Computational Linguis-tics,2004:30 (3):277-308.
[113]Yu H., Hatzivassiloglou,V. Towards answering opinion questions:Separating Facts from opinions and identifying the Polarity of opinion sentences[C]//Conference on EmPirical Methods in Natural Language Processing (EMNLP).
[114]A.Popescu, O.Etzioni. Extracting Product Features and Opinions from Reviews[C]// Proceedings of HLT/EMNLP 2005.Vancouver:ACL,2005.
[115]M.Hu,B.Liu. Mining Opinion Features in Customer Reviews[C]//Proceedings of Nineteeth National Conference on Artificial Intellgience.San Jose:AAAI,2004:755-760.
[116]Kim, SooMin, Eduard Hovy.2005. Identifying Opinion Holders for Question Answering in Opinion Texts[C]. Proceedings of AAAI-05 Workshop on Question Answering in Restricted Domains,2005.
[117]Meena,A., Prabhakar, T. Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis[J] ECIR 4425:573-580.
[118]叶强,张紫琼,罗振雄.面向互联网评论情感分析的中文主观性自动判别方法研究[J].信息系统学报,2007,1(1)：79-91.
[119]姚天昉,娄德成.汉语情感词语义倾向判别的研究[C]//第七届中文信息处理国际会议,2007,222-225.
[120]熊德兰,程菊明,田胜利.基于HowNet的句子褒贬倾向性研究[J].计算机工程与应用,2008,44(22)：143-144.
[121]李实,叶强,李一军.中文网络客户评论的产品特征挖掘方法研究[J].管理科学学报,2009,12(2)：142-152.
[122]党蕾,张蕾.一种基于知网的中文句子情感倾向判别方法[J].计算机应用研究,2010,27(4)：1370-1372.
[123]Yi Mao, Guy Lebanon. Sequential Models for Sentiment Prediction[C]//Proceedings of the ICML Workshop on Learning in Structured Output Spaces, Pittsburgh, PA,2006.
[124]Ryan McDonald, Kerry Hannan and Tyler Neylon et al. Structured Models for Fine-to-Coarse Sentiment Analysis[C]. In:Proceedings of ACL.2007.432-439.
[125]Y.Qiang, S.Wen, L.Yijun. Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach[C]//Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Volume 03,2006.
[126]孟凡博,蔡莲红,陈斌等.文本褒贬倾向判定系统的研究[J].小型微型计算机系统, 2009,30(7)：1458-1461.
[127]李寿山,黄居仁.基于Stacking组合分类方法的中文情感分类研究[J].中文信息学报,2010(5)：56-61
[128]吴琼,谭松波,程学旗.中文情感倾向性分析的相关研究进展[J].信息技术快报,2010,8(4)：16-38.
[129]赵军,许洪波,黄萱菁等.中文倾向性分析评测技术报告[C]//第一届中文倾向性分析评测(COAE2008),2008.
[130]Abbasi A.. Chen, H., Salem A. Sentiment analysis in multiple languages:Feature selection for opinion classification in Web forums [J]. ACM Transactions on Information 2008,26(3).
[131]周杰,林琛,李弼程.基于机器学习的网络新闻评论情感分类研究[J].计算机应用,2010,30(4)：1011-1014.
[132]白鸽,左万利,赵乾坤.使用机器学习对汉语评论进行情感分类[J].吉林大学学报,2009.11,1260-1263.
[133]Berger, A., Pietra,V., Pie tra S. A Maximum Entropy Approach to Natural Languae Processing[J]. Computational Linguistics,1996,22(1):39-71.
[134]S.Kim, E.Hovy. Identifying and Analyzing Judgment Opinions[C]//Proceedings of HLT/NAACL-2006. New York City,2006.
[135]Qiang Ye, Wen Shi, Yijun Li Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach[C]//Proceedings of the 39th Hawaii International Conference on System Sciences,2006.
[136]王素格.基于Web的评论文本情感分类问题研究[D].上海大学博十学位论文2008.4.
[137]王素格,杨军玲,张武.自动获取汉语词语搭配[J].中文信息学报2006；20(31-37).
[138]下载地址：http://www.searchforum.org.cn/tansongbo/senticorpus.jsp
[139]汪传雷.关于企业危机信息管理研究的几个问题[J].情报科学,2005(8)：77-80.
[140]邱均平.网络信息计量学[M].2007.1武汉大学出版社出版.
[141]秦州.新闻搜索中的舆情“峰值”——中国近年来重大矿难报道Web页面数分析[J].新闻界,2005,5：94-96.
[142]谢海光,陈中润.互联网内容及舆情深度分析模式[J].中国青年政治学院学报,2006,25(3)：95-100.
[143]魏玖长危机事件社会影响的分析与评估研究[D].合肥,中国科学技术大学,2006.
[144]Rowe G, Hawkes G, Houghton J. Initial UK Public Reaction to Avian Influenza:Analysis of Opinions Posted on the BBC W ebsite [J]. Health, Risk & Society Health, Risk& Society, 2008,10(4):361-384.
[145]孙佰清,董靖巍.重大公共危机网络舆情扩散监测和规律分析[J].哈尔滨工业大学学报(社会科学版),2011,13(1)：92-97.
[146]齐海凤网络舆情热点发现与事件跟踪技术研究[D].哈尔滨,哈尔滨工程大学,2008.
[147]刘恒文基于网络语义挖掘的舆情检测预警研究[D].武汉,武汉理工大学,2010.
[148]陆题佳.互联网中危机信息传播规律及应对模式研究[D].合肥,中国科学技术大学,2010
[149]潘崇霞.网络舆情演化的阶段分析[J].计算机与现代化,2011.10：203-206.
[150]Engle, Robert F. Autoregressive Conditional Heteroskedasticity with Estimates of Variance of U.K. Inflation. Econometrica,1982,50:987-1008.
[151]Bollerslev, Tim. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics,1986,31:307-327.
[152]Engle, Robert F. and Bollerslev, Tim. Modeling the Persistence of Conditional Variances, Econometric Reviews,1986,5:1-50.
[153]伊丽莎白·诺尔·诺伊曼.《大众观念理论：沉默的螺旋的概念》[A].中国社会科学出版社,2000,10.235.
[154]夏志梅,彭光芒.网络空间中的“沉默的螺旋”理论的局限性[J].北京理工大学学报(社会科学版)2006.3.
[155]谢新洲.“沉默的螺旋”假说在互联网环境下的实证研究[J].信息与传播理论,2003：17-22.
[156]蒋宇.“沉默的螺旋”与网络传播[J]重庆交通大学学报(社科版),2007(7)：61-63.
[157]Nelson, Daniel B. Conditional Heteroskedasticity in Asset:A New Approach. Econometrica,1991,59:347-370.
[158]R. Rabemananjara, J. M. Zakoian. Threshold arch models and asymmetries in volatility[J] Journal of Applied Econometrics,1993,8(1):31-49.
[159]Zakoian, J.M. Threshold Heteroseedastic Models [J]. Journal of Economic Dynamics and Control,18:931-955.
[2]徐晓日.网络舆情事件的应急处理研究[J].华中电力大学学报(社会科学版),2007(1)：89-93.
[3]刘毅.网络舆情研究概论[M].天津人民出版社,2007.
[4]中国互联网络发展状况统计报告,中国互联网络信息中心,http://www.cnnic.cn/research/
[5]下载地址:http://www.iricn.com/index.php?option=com content&view=article&id= 258&Itemid=7
[6]曾润喜.网络舆情突发事件预警系统、指标与机制[J].情报杂志,2009,11：52-54.
[7]Yang,Y., Pedersen,J. A comparative Study on Feature Selection in Text Categorization[C]// ICML 1997,412-420.
[8]Liu,H.,Motoda,H. Feature Extraction, Construction and Selection:A Data Mining Perspective. Kluwer Academic, Norwell, MA, USA,1998.
[9]V. Vapnik, Statistical learning theory [M]. Wiley,1998.
[10]Joachims,T. Text categorization with support vector machines:learning with many relevant features.[C]//Proceedings of ECML-98,10th European Conference on Machine Learning, 137-142,1998.Springer Verlag.
[11]S.C.Deewester, S.T. Dumais, T.K. Landauer. Indexing by latent semantic analysis[J]. Journal of the American Society of Information Science,1990,41(6):391-407.
[12]Susan T. Dumais. Latent semantic indexing (lsi) and Trec-2[J]. NIST SPECIAL PUBLI-CATION SP,1994-trec.nist.gov.
[13]Kontostathis,A., Pottenger,W. A framework for understanding Latent Semantic Indexing (LSI) performance [J]. Information Processing and Management,2006,42(1):56-73.
[14]Picard RW. Affective computing [M]. MIT Press, Cambridge, MA,1997.
[15]Sentiment analysis [EB/OL].http://en.wikiPedia.org/wiki/Sentiment_analysis.
[16]赵军,许洪波,黄萱菁等.中文倾向性分析评测技术报告[C]//第一届中文倾向性分析评测研讨会,2008,1-20.
[17]Sznajd-Weron K., Sznajd J. Opinion Evolution in Closed Community [J].International Journal of Modern Physics,2000,11 (6):1157-1165.
[18]邱均平.网络信息计量学[M].武汉大学出版社出版2007.1
[19]Jon Kleinberg. Bursty and Hierarchical Structure in Streams[J]. Data Mining and Knowledge Discovery,2007,7(4):373-397.
[20]刘毅.内容分析法在网络舆情信息分析中的应用[J].天津大学学报：社会科学版,2006(7)：307-310.
[21]下载地址：http://www.iricn.com/index.php?option=com content&view=article&id=215& Itemid=7
[22]Brin S, L Page. The Anatomy of a Large-Scale HyPertextual WebSearch Engine[J]. Computer Networks and ISDN Systems,1998,30(1):107-117.
[23]Heydon A, M Najork. Mercator:A Scalable, Extensible Web Crawler[J]. World Wide Web, 1999,2(4):219-229
[24]刘金红,陆余良.主题网络爬虫研究综述[J].计算机应用研究,2007,24(10)：26-30
[25]Chakrabarti.S, Martin,V., B Dom. Focused Crawling:A New Approach to Topic-specific Web Resource Discovery [J]. Computer Networks,1999,31(11):1623-1640.
[26]J Allan, J Carbonell, G Doddington, et al. Topic detection andtracking pilot study:Final report[C]//Proceedings of the DARPA Broadcast NewsTranscription and Understanding Workshop, Virginia:Lansdowne,February 1998,194-218.
[27]J Allan, R Papka, V Lavrenko.On-line New Event Detection and Tracking[C]//Proceedings of SIGIR'98.University of Massachusetts:Amherst,1998,37-45.
[28]YB Liu, Cai, JR, J Yin, AWC Fu. Clustering Text Data Streams[J]. Journal of Computer Science and Technology,2008,23(1):112-128.
[29]XY Li, WB Croft. Improving novelty detection for general topics using sentence level information patterns[C]//Proceedings of the 15th ACM international conference on Information and knowledge management, Arlington, Virginia,2006:238-247
[30]RT Fernandez, DE Losada. Novelty detection using local context analysis[C]//Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, The Netherlands,2007:725-726.
[31]王允.网络舆情数据获取与话题分析技术研究[D]郑州,解放军信息工程大学,2010.6
[32]刘玉国.基于内容的互联网舆情信息挖掘关键技术研究[D]济南,山东大学,2011.4
[33]Y Yang, T Pierce, J Carbonell. A study on Retrospective and On-Line Event detection[C]// Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval.1998, CMU, USA:ACM,28-36.
[34]R Papka. On-line New Event Detection, Clustering and Tracking. Amherst:Department of Computer Science, UMASS,1999.
[35]J Allan, R Papka, V Lavrenko.On-Line New Event Detection and Tracking[C]//Proceedings of SIGIR'98:21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM Press,1998,37-45.
[36]Y Yang, T Pierce, J Carbonell. A study on Retrospective and On-Line Event detection[C]// Proceedings of the 21st international ACM SIGIR conference on Research and development in information retrieval.1998,CMU, USA:ACM,28-36.
[37]Y Zhang, JG Carbonell, J Allan. Topic Detection and Tracking:Detection-Task[C]// Proceedings of the Workshop of Topic Detection and Tracking,1997.
[38]J Carbonell, Y Yang, J Lafferty, et al. CMU Report on TDT-2:Segmentation, Detection and Tracking[C]//Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. SanFrancisco:Morgan Kauffman,1999,117-120.
[39]JM Schultz and M Liberman. Topic detection and tracking using idf-weighted cosine coefficient[C]//Proceedings of the DARPA Broadcast News Workshop. San Francisco: Morgan Kaufmann,1999,189-192.
[40]郭岩,白硕,于满泉.Web使用信息挖掘综述[J].计算机科学,2005,32(1)：1-7.
[41]General Inquirer [EB/OL]. http://www.wjh.harvard.edu/-Inquirer/
[42]Fellbaum C. WordNet:An Electronic Lexical Database[M].BradfordBook.1998.
[43]Esuli A. Sebastiani F. SentiwordNet:A Publicly available lexical resource for Opinion mining[C]//In Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation. Genova:IT2006.
[44]HowNetIM]. HowNet's HomePage. http://www.keenage.com
[45]梅家驹,竺一鸣,高蕴琦等.同义词辞林[M].第二版,上海：上海辞书出版社,1996.
[46]姚天防,娄德成.汉语情感词语义倾向判别的研究[C]//第七届中文信息处理国际会议,2007.
[47]柳位平,朱艳辉等.中文基础情感词词典构建方法研究[J].计算机应用,2009,29(10)：2875-2877.
[48]杜伟夫文本倾向性分析中的情感词典构建技术研究[D].哈尔滨,哈尔滨工业大学,2010.4.
[49]吴琼,谭松波,张刚等.跨领域倾向性分析相关技术研究[J].中文信息学报,2010,24(1)：77-84.
[50]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:2002:79-86.
[51]MULLEN T, COLL IER N. Sentiment analysis using support vector machiness with diverse information sources[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Barcelona:NLP,2004:412-418.
[52]J.Wiebe, E.Riloff. Creating subjective and objective sentence classifiers from unannotated text[C]//The 6th International Conference on computational Linguistics and Intelligent Text Processing,2005:486-497.
[53]唐慧丰,谭松波,程学旗.基于监督学习的中文情感分类技术比较研究中文信息学报,2007.11.
[54]徐军,丁宇新,王晓龙.使用机器学习方法进行新闻的情感自动分类[J].中文信息学报,2007,21(6)：95-100.
[55]罗亚平,王机,周延泉.基于关注度的热点话题发现模型[C]//In Proceeding of ICCC2007 workshop.402-408.
[56]李思,张浩,徐蔚然.基于合并模型的中文文本情感分析[C]//《第五届全国信息检索学术会议论文集》,2009：322-328.
[57]胡熠,陆汝占,李学宁等.基于语言建模的文本情感分类研究[J].计算机研究与发展,2007,44(9)：1469-1475.
[58]孙玉芬,卢炎生.流数据挖掘综述[J].计算机科学,2007,34(1)：1-5.
[59]袁志坚,王乐,田李等.数据流突发检测研究与进展[J].计算机工程与应用,2008,44(21)：1-4
[60]Toshiaki F., Tomoyuki N., Yasuhiro S. Identification of Bursts in a Document Stream[C]// The first International Workshop on Knowledge Discovery in Data Streams, Pisa,Italy,2004
[61]中华人民共和国突发事件应对法[EB/OL].http://www.gov.cn/ziliao/flfg/2007-08/30/ content 732593. htm.
[62]袁辛奋,胡子林.浅析突发事件的特征、分类和意义[J].科技与管理,2005.2 23-25
[63]He H, Chen B, Du L, et al. PRIS in TREC 2008 Blog Track[C]//TREC 2008.
[64]G.Salton, A.Wong C.S.Yang. A vector space model for automatic indexing[C]//Communica-tions of ACM 1975 (18),613-620,1975
[65]Salton G. McGill,M. Introduction to Modern Information Retrieval[M].McGraw-Hill.1983
[66]Yang,Y, Pedersen J.O. A comparative study on feature selection in text categorization. The 14th International Conference on Machine Learning,1997:412-420.
[67]Mladenic D, Grobelnik M. Feature selection for classification based on text hierarchy. Workshop on Learning from Text and Web.1998.
[68]Church K.W, Hanks P. Words association norms, mutual information and lexicography [J]. Computational Linguistics,1989,16(1):22-29.
[69]Dunning T. E. Accurate methods for the statistics of surprise and coincidence[J]. Computational Linguistics,1993,19(1):61-74.
[70]Ruizetal M. E. Automatic text categorization using neural networks[C]//The 8th ASIS SIG/ CR Classification Research,1997,8:59-72.
[71]Nigam K, Lafferty J, McCallum A. Using maximum entropy for text classification[C]//IJCAI Workshop on Machine Learning for Information Filtering,1999:61-67.
[72]Tokunaga T, Iwayama M. Text categorization based on weighted inverse document frequency [C]//SIG-IPS Japan,1994,100(5)
[73]Yang Y, Pedersen J.O. A comparative study on feature selection in text categorization[C]// The 14th International Conference on Machine Learning,1997:412-420.
[74]George Forman. An extensive empirical study of feature selection metrics for text classification [J].The Journal of Machine Learning Research archive,2003,3.
[75]Rogati M, Yang Y. High-performing feature selection for text classification[C]//CIKM'02, 2002:659-661.
[76]谭松波.高性能文本分类算法研究[D].中国科学院研究生院2006.
[77]单丽莉,刘秉权,孙承杰.文本分类中特征选择方法的比较与改进[J].哈尔滨工业大学学报2011,43(3)：321-326.
[78]Yan g M H. Kernel eigenfaces vs. kernel Fisherfaces:Facer ecognition using kernel methods [C]//Proceedin gs of the 5th IEEE Internat ional Conference on Automatic Face and Gesture Recognition. Was hington, DC, IEEE Computer Society,2002:215-220.
[79]Lu J W, Plataniot is K N, Venet sanopoulos A N. Face recognition using kernel direct discriminant analysis algorithms[J].IEEE Transactions on Neural Networks,2003,14(1): 117-126.
[80]吴成富,马松辉,陈怀民.地形匹配可信度分析[J].西北工业大学学报,2004,22(3)：346-349.
[81]Xiong T, Ye J P, Li Q, et al. Efficient kernel discriminant analysis via QR decomposition// Advances in Neural Information Processing Systems 17,2005.
[82]谭松波中文语料TanCorpV1.0,下载地址:http://www.searchforum.org.cn/tansongbo /corpus.htm.
[83]杜阿宁.互联网舆情信息挖掘方法研究[D]哈尔滨,哈尔滨工业大学,2007.
[84]赵旭东.互联网舆情指数挖掘方法[D]哈尔滨,哈尔滨工业大学,2007.
[85]Deerwester, S., Dumais, S., Landauer, T. et.al. Indexing by latent semantic analysis[J]. Journal of the Society for Information Science,41(6):391-407,1990.
[86]Dumais S. Using LSA for information filtering:TREC-3 experiments[C]//The Third Text Retrieval Conference (TREC-3). National Institute of Standards and Technology Special Publication,219-230.
[87]Landauer,T.,Foltz,P.,Laham,D. Introduction to latent semantic analysis [J]. Discourse Proce-sses,1998,25,259-284.
[88]Rosenstein M., Lochbaum C., Recommending from content:preliminary results from an e-commerce experiment[C]//Proceedings of Conference on Human Factors in Computing, 2000.
[89]Zelikovitz S., Hirsh H. Using LSI for Text Classification in the Presence of Background Text[C]//Proceedings of CIKM01,113-118,2001.
[90]Kitajima, M., Kariya, N., Takagi, H., & Zhang, Y. (2005). Evaluation of website usability using Markov chains and latent semantic analysis. IEICE Transactions on Communications, E88B(4),1467-1475.
[91]王永智,藤至阳,王鹏等.基于LSA和SVM的文本分类模型的研究[J].计算机工程与设计,2009,30(3)：729-734.
[92]王国勇,许建锁.一种基于LSA和Kohonen网络的文本分类新方法[J].计算机应用,2004,24(2)：53-56.
[93]Bo Yu, Zong-ben Xu, Cheng-hua Li. Latent semantic analysis for text categorization using neural network [J], Knowledge-Based Systems,21 (2008) 900-904.
[94]McCarthy P., Briner S., Rus V. Textual Signatures:identifying text-types using Latent Semantic Analysis to measure the cohesion of text structures[J].Natural Language Processing and Text Mining, Springer-Verlag, London,2006:107-122.
[95]张玉峰,何超.基于潜在语义分析和HS-SVM的文本分类模型研究[J].情报理论与实践,2010,33(7)：104-107.
[96]刘昌钰,唐常杰,于中华等.基于潜在语义分析的BBS文档Bayes鉴别器[J].计算机学报,2004,27(4)：566-572.
[97]潘渊,李弼程,张先飞LS-SVM：一种有效的新闻主题追踪方法[J].计算机应用研究,2008,25(9)：2661-2663.
[98]Hull, D, Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing// Proceedings of SIGIR'94, pp.282-289,1994.
[99]LIU T, CHEN Z, ZHANG BY, et al. Improving Text Classification using Local Latent Semantic Indexing[A]. Proceedings of the 4th IEEE International Conference on Data Mining [C]//2004.162-169.
[100]余正涛,樊孝忠,郭剑毅等.基于潜在语义分析的汉语问答系统答案提取[J].计算机学报,2006,29(10)：1889-1893.
[101]Schutze, H., Silverstein, C. Projections for Efficient Document Clustering[C]//Proceeding of SIGIR'97,1997,74-81.
[102]Wu, H., Gunopulos D. Evaluating the Utility of Statistical Phrases and Latent Semantic Indexing for Text Classification[C]//Proc. of ICDM'02,713-716,2002.
[103]Hatzivassiloglou V., McKeown K R. Predicting the semantic orientation of adjectives[C] //Proceedings of EACL. Morristown:ACL,1997.174-181.
[104]P.D.Turney. Thumbs up Or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews[C]//Proceedings of 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia:ACL,2002:417-424.
[105]梅家驹,竺一鸣,高蕴琦等.同义词辞林[M].第二版,上海：上海辞书出版社,1996.
[106]朱嫣岚,闵锦,周雅倩等.基于hownet的词汇语义倾向计算[J].中文信息学报,2006,20(1)：14-20.
[107]Jiang M, Xiao Sb, Wang Hw, et al. An improved word similarity computing method based on Hownet[J]. Journal of Chinese Information Processing,2008,22 (5):84289.
[108]徐琳宏,林鸿飞,杨志豪.基于语义理解的文本倾向性识别机制[J].中文信息学报,2007,21(1)：96-100.
[109]路斌,万小军,杨建武等.基于同义词词林的词汇褒贬计算[C]//第七届中文信息处理国 际会议论文集.2007：17-23.
[110]Weifu Du, Songbo Tan. Optimizing Modularity to Identify the Semantic Orientation of Chinese Words[J]. Expert Systems with Applications.2010,37(7):5094-5100.
[111]Wiebe, J. Learning subjective adjectives from corpora[C]//InProceeding of the 17th National Conference on Artificial Intelligence,Calif, AAAI Press,2000:735-740.
[112]Wiebe,J., WilsonT., BrueeR.et.al. Learning subjective language[J].Computational Linguis-tics,2004:30 (3):277-308.
[113]Yu H., Hatzivassiloglou,V. Towards answering opinion questions:Separating Facts from opinions and identifying the Polarity of opinion sentences[C]//Conference on EmPirical Methods in Natural Language Processing (EMNLP).
[114]A.Popescu, O.Etzioni. Extracting Product Features and Opinions from Reviews[C]// Proceedings of HLT/EMNLP 2005.Vancouver:ACL,2005.
[115]M.Hu,B.Liu. Mining Opinion Features in Customer Reviews[C]//Proceedings of Nineteeth National Conference on Artificial Intellgience.San Jose:AAAI,2004:755-760.
[116]Kim, SooMin, Eduard Hovy.2005. Identifying Opinion Holders for Question Answering in Opinion Texts[C]. Proceedings of AAAI-05 Workshop on Question Answering in Restricted Domains,2005.
[117]Meena,A., Prabhakar, T. Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis[J] ECIR 4425:573-580.
[118]叶强,张紫琼,罗振雄.面向互联网评论情感分析的中文主观性自动判别方法研究[J].信息系统学报,2007,1(1)：79-91.
[119]姚天昉,娄德成.汉语情感词语义倾向判别的研究[C]//第七届中文信息处理国际会议,2007,222-225.
[120]熊德兰,程菊明,田胜利.基于HowNet的句子褒贬倾向性研究[J].计算机工程与应用,2008,44(22)：143-144.
[121]李实,叶强,李一军.中文网络客户评论的产品特征挖掘方法研究[J].管理科学学报,2009,12(2)：142-152.
[122]党蕾,张蕾.一种基于知网的中文句子情感倾向判别方法[J].计算机应用研究,2010,27(4)：1370-1372.
[123]Yi Mao, Guy Lebanon. Sequential Models for Sentiment Prediction[C]//Proceedings of the ICML Workshop on Learning in Structured Output Spaces, Pittsburgh, PA,2006.
[124]Ryan McDonald, Kerry Hannan and Tyler Neylon et al. Structured Models for Fine-to-Coarse Sentiment Analysis[C]. In:Proceedings of ACL.2007.432-439.
[125]Y.Qiang, S.Wen, L.Yijun. Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach[C]//Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Volume 03,2006.
[126]孟凡博,蔡莲红,陈斌等.文本褒贬倾向判定系统的研究[J].小型微型计算机系统, 2009,30(7)：1458-1461.
[127]李寿山,黄居仁.基于Stacking组合分类方法的中文情感分类研究[J].中文信息学报,2010(5)：56-61
[128]吴琼,谭松波,程学旗.中文情感倾向性分析的相关研究进展[J].信息技术快报,2010,8(4)：16-38.
[129]赵军,许洪波,黄萱菁等.中文倾向性分析评测技术报告[C]//第一届中文倾向性分析评测(COAE2008),2008.
[130]Abbasi A.. Chen, H., Salem A. Sentiment analysis in multiple languages:Feature selection for opinion classification in Web forums [J]. ACM Transactions on Information 2008,26(3).
[131]周杰,林琛,李弼程.基于机器学习的网络新闻评论情感分类研究[J].计算机应用,2010,30(4)：1011-1014.
[132]白鸽,左万利,赵乾坤.使用机器学习对汉语评论进行情感分类[J].吉林大学学报,2009.11,1260-1263.
[133]Berger, A., Pietra,V., Pie tra S. A Maximum Entropy Approach to Natural Languae Processing[J]. Computational Linguistics,1996,22(1):39-71.
[134]S.Kim, E.Hovy. Identifying and Analyzing Judgment Opinions[C]//Proceedings of HLT/NAACL-2006. New York City,2006.
[135]Qiang Ye, Wen Shi, Yijun Li Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach[C]//Proceedings of the 39th Hawaii International Conference on System Sciences,2006.
[136]王素格.基于Web的评论文本情感分类问题研究[D].上海大学博十学位论文2008.4.
[137]王素格,杨军玲,张武.自动获取汉语词语搭配[J].中文信息学报2006；20(31-37).
[138]下载地址：http://www.searchforum.org.cn/tansongbo/senticorpus.jsp
[139]汪传雷.关于企业危机信息管理研究的几个问题[J].情报科学,2005(8)：77-80.
[140]邱均平.网络信息计量学[M].2007.1武汉大学出版社出版.
[141]秦州.新闻搜索中的舆情“峰值”——中国近年来重大矿难报道Web页面数分析[J].新闻界,2005,5：94-96.
[142]谢海光,陈中润.互联网内容及舆情深度分析模式[J].中国青年政治学院学报,2006,25(3)：95-100.
[143]魏玖长危机事件社会影响的分析与评估研究[D].合肥,中国科学技术大学,2006.
[144]Rowe G, Hawkes G, Houghton J. Initial UK Public Reaction to Avian Influenza:Analysis of Opinions Posted on the BBC W ebsite [J]. Health, Risk & Society Health, Risk& Society, 2008,10(4):361-384.
[145]孙佰清,董靖巍.重大公共危机网络舆情扩散监测和规律分析[J].哈尔滨工业大学学报(社会科学版),2011,13(1)：92-97.
[146]齐海凤网络舆情热点发现与事件跟踪技术研究[D].哈尔滨,哈尔滨工程大学,2008.
[147]刘恒文基于网络语义挖掘的舆情检测预警研究[D].武汉,武汉理工大学,2010.
[148]陆题佳.互联网中危机信息传播规律及应对模式研究[D].合肥,中国科学技术大学,2010
[149]潘崇霞.网络舆情演化的阶段分析[J].计算机与现代化,2011.10：203-206.
[150]Engle, Robert F. Autoregressive Conditional Heteroskedasticity with Estimates of Variance of U.K. Inflation. Econometrica,1982,50:987-1008.
[151]Bollerslev, Tim. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics,1986,31:307-327.
[152]Engle, Robert F. and Bollerslev, Tim. Modeling the Persistence of Conditional Variances, Econometric Reviews,1986,5:1-50.
[153]伊丽莎白·诺尔·诺伊曼.《大众观念理论：沉默的螺旋的概念》[A].中国社会科学出版社,2000,10.235.
[154]夏志梅,彭光芒.网络空间中的“沉默的螺旋”理论的局限性[J].北京理工大学学报(社会科学版)2006.3.
[155]谢新洲.“沉默的螺旋”假说在互联网环境下的实证研究[J].信息与传播理论,2003：17-22.
[156]蒋宇.“沉默的螺旋”与网络传播[J]重庆交通大学学报(社科版),2007(7)：61-63.
[157]Nelson, Daniel B. Conditional Heteroskedasticity in Asset:A New Approach. Econometrica,1991,59:347-370.
[158]R. Rabemananjara, J. M. Zakoian. Threshold arch models and asymmetries in volatility[J] Journal of Applied Econometrics,1993,8(1):31-49.
[159]Zakoian, J.M. Threshold Heteroseedastic Models [J]. Journal of Economic Dynamics and Control,18:931-955.