一种基于话题聚类及情感强度的微博舆情分析模型
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摘要
【目的】构建一种微博舆情热点的监控和预测模型,从话题聚类及情感强度的角度出发解决短文本漂移、情感极性量化等问题。【方法】提出一种基于话题聚类及情感强度的微博舆情分析模型,实现微博话题快速聚类及情感强度量化计算,通过时序回归分析追踪预测热点话题的情感变化。【结果】实验结果表明,本文模型预测准确率达88.97%,对比i Lab-Edinburgh模型提高约7%,证明了模型的可行性。【局限】未考虑突发事件下,模型对于事件的预警预测效果。【结论】本文模型能够有效提高公众情感倾向的预测准确性,为微博舆情分析方法提供新的途径。
[Objective] This paper builds a model to monitor the trending topics from microblogs, aiming to deal with the issues of text drifting and quantitation of sentimental polarity. [Methods] First, we proposed a public opinion analysis model based on topic clustering and sentiment intensity. Then, we used the time series regression analysis to predict the sentimental changes among the trending topics. [Results] The prediction accuracy of our model reached 88.97%, which was about 7% higher than the i Lab-Edinburgh model. [Limitations] More research is needed to study the early warning mechanisms for emergency events. [Conclusions] The proposed model could improve the prediction accuracy of sentimental changes, which provides an effective way to analyze the public opinion from microblogs.
[Objective] This paper builds a model to monitor the trending topics from microblogs, aiming to deal with the issues of text drifting and quantitation of sentimental polarity. [Methods] First, we proposed a public opinion analysis model based on topic clustering and sentiment intensity. Then, we used the time series regression analysis to predict the sentimental changes among the trending topics. [Results] The prediction accuracy of our model reached 88.97%, which was about 7% higher than the i Lab-Edinburgh model. [Limitations] More research is needed to study the early warning mechanisms for emergency events. [Conclusions] The proposed model could improve the prediction accuracy of sentimental changes, which provides an effective way to analyze the public opinion from microblogs.
引文
[1]马晓玲,金碧漪,范并思.中文文本情感倾向分析研究[J].情报资料工作,2013(1):52-56.(Ma Xiaoling,Jin Biyi,Fan Bingsi.An Analysis of Chinese Text Emotional Tendency[J].Information and Documentation Service,2013(1):52-56.)
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[3]唐晓波,罗颖利.融入情感差异和用户兴趣的微博转发预测[J].图书情报工作,2017,61(9):102-110.(Tang Xiaobo,Luo Yingli.Integrating Emotional Divergence and User Interests into the Prediction of Microblog Retweeting[J].Library and Information Service,2017,61(9):102-110.)
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[8]何跃,肖敏,张月.结合话题相关性的热点话题情感倾向研究[J].数据分析与知识发现,2017,1(3):46-53.(He Yue,Xiao Min,Zhang Yue.Sentiment Analysis of Trending Topics Based on Relevance[J].Data Analysis and Knowledge Discovery,2017,1(3):46-53.)
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[11]李慧,柴亚青.基于属性特征的评论文本情感极性量化分析[J].数据分析与知识发现,2017,1(10):1-11.(Li Hui,Chai Yaqing.Analysis Sentiment Polarity of Comments Based on Attributes[J].Data Analysis and Knowledge Discovery,2017,1(10):1-11.)
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[13]郑丽娟,王洪伟,郭恺强.基于情感词模糊统计的网络评论情感强度的研究[J].系统管理学报,2014,23(3):324-330.(Zheng Lijuan,Wang Hongwei,Guo Kaiqiang.Sentiment Intensity of Online Reviews Based on Fuzzy-Statistics of Sentiment Words[J].Journal of Systems&Management,2014,23(3):324-330.)
[14]Pérez-Ortiz M,Gutiérrez P A,Carbonero-Ruz M,et al.Semi-supervised Learning for Ordinal Kernel Discriminant Analysis[J].Neural Networks,2016,84:57-66.
[15]周航星,陈松灿.有序判别典型相关分析[J].软件学报,2014,25(9):2018-2025.(Zhou Hangxing,Chen Songcan.Ordinal Discriminative Canonical Correlation Analysis[J].Journal of Software,2014,25(9):2018-2025.)
[16]Hotelling H.Relations Between 2 Sets of Variants[J].Biometrika,1935,28(3-4):312-377.
[17]Yoshida K,Yoshimoto J,Doya K.Sparse Kernel Canonical Correlation Analysis for Discovery of Nonlinear Interactions in High-dimensional Data[J].BMC Bioinformatics,2017,18(1):108-118.
[18]钟敏娟,万常选,刘德喜.基于关联规则挖掘和极性分析的商品评论情感词典构建[J].情报学报,2016,35(5):501-509.(Zhong Minjuan,Wan Changxuan,Liu Dexi.Opinion Lexicon Construction Based on Association Rule and Orientation Analysis for Production Review[J].Journal of the China Society for Scientific and Technical Information,2016,35(5):501-509.)
[19]刘德喜.情感词扩展对微博情感分类性能影响的实验分析[J].小型微型计算机系统,2016,37(5):957-965.(Liu Dexi.Effect of Sentimental Word Expansion on the Performance of Microblog Sentiment Classification Task[J].Journal of Chinese Computer Systems,2016,37(5):957-965.)
[20]阳林.情感词权值研究及在情感极性分析中的应用[J].计算机应用,2015,35(S2):125-127.(Yang Lin.Emotional Term Weight Research and Application to Emotional Polarity Analysis[J].Journal of Computer Applications,2015,35(S2):125-127.)
[21]Van Arthur G,Staals F,L?ffler M,et al.Multi-Granular Trend Detection for Time-Series Analysis[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(1):661-670.
[22]唐晓波,童海燕,严承希.基于话题情感强度的微博舆情分析[J].图书馆学研究,2014(17):85-93.(Tang Xiaobo,Tong Haiyan,Yan Chengxi.Microblogging Public Opinion Analysis Based on Emotional Intensity of the Topic[J].Research on Libray Science,2014(17):85-93.)
[23]Refaee E,Rieser V.i Lab-Edinburgh at Sem Eval-2016 Task 7:A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases[C]//Proceedings of the 10th International Workshop on Semantic Evaluation.2016.
[2]Vaibhavi N,Patodkar N P,Shaikh I R.Sentimental Analysis on Twitter Data Using Naive Bayes[C]//Proceedings of the 6th Post Graduate Conference for Computer Engineering.2017.
[3]唐晓波,罗颖利.融入情感差异和用户兴趣的微博转发预测[J].图书情报工作,2017,61(9):102-110.(Tang Xiaobo,Luo Yingli.Integrating Emotional Divergence and User Interests into the Prediction of Microblog Retweeting[J].Library and Information Service,2017,61(9):102-110.)
[4]Ingle M M,Emmanues M.Evaluations on Sentiment Analysis of Micro Blogging Site Using Topic Modeling[C]//Proceedings of the 2016 International Conference on Signal Processing,Communication,Power and Embedded System.2016.
[5]Giatsoglou M,Vozalis M G,Diamantaras K,et al.Sentiment Analysis Leveraging Emotions and Word Embeddings[J].Expert Systems with Applications,2017,69:214-224.
[6]韩忠明,陈妮,乐嘉锦,等.面向热点话题时间序列的有效聚类算法研究[J].计算机学报,2012,35(11):2337-2347.(Han Zhongming,Chen Ni,Le Jiajin,et al.An Efficient and Effective Clustering Algorithm for Time Series of Hot Topics[J].Chinese Journal of Computers,2012,35(11):2337-2347.)
[7]吴青林,周天宏.基于话题聚类及情感强度的中文微博舆情分析[J].情报理论与实践,2016,39(1):109-112.(Wu Qinglin,Zhou Tianhong.Public Opinion Analysis of Chinese Microblog Based on Topic Clustering and Emotion Intensity[J].Information Studies:Theory&Application,2016,39(1):109-112.)
[8]何跃,肖敏,张月.结合话题相关性的热点话题情感倾向研究[J].数据分析与知识发现,2017,1(3):46-53.(He Yue,Xiao Min,Zhang Yue.Sentiment Analysis of Trending Topics Based on Relevance[J].Data Analysis and Knowledge Discovery,2017,1(3):46-53.)
[9]Sotiropoulos D N,Kounavis C D,Kourouthanassis P,et al.What Drives Social Sentiment?An Entropic Measure-based Clustering Approach Towards Identifying Factors that Influence Social Sentiment Polarity[C]//Proceedings of the5th International Conference on Information,Intelligence,Systems and Applications.2014.
[10]Manek A S,Shenoy P D,Mohan M C,et al.Aspect Term Extraction for Sentiment Analysis in Large Movie Reviews Using Gini Index Feature Selection Method and SVM Classifier[J].World Wide Web,2017,20(2):135-154.
[11]李慧,柴亚青.基于属性特征的评论文本情感极性量化分析[J].数据分析与知识发现,2017,1(10):1-11.(Li Hui,Chai Yaqing.Analysis Sentiment Polarity of Comments Based on Attributes[J].Data Analysis and Knowledge Discovery,2017,1(10):1-11.)
[12]Meisheri H,Saha R,Sinha P,et al.Textmining at Emo Int-2017:A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets[C]//Proceedings of the8th Workshop on Computational Approaches to Subjectivity,Copenhagen,Denmark.2017.
[13]郑丽娟,王洪伟,郭恺强.基于情感词模糊统计的网络评论情感强度的研究[J].系统管理学报,2014,23(3):324-330.(Zheng Lijuan,Wang Hongwei,Guo Kaiqiang.Sentiment Intensity of Online Reviews Based on Fuzzy-Statistics of Sentiment Words[J].Journal of Systems&Management,2014,23(3):324-330.)
[14]Pérez-Ortiz M,Gutiérrez P A,Carbonero-Ruz M,et al.Semi-supervised Learning for Ordinal Kernel Discriminant Analysis[J].Neural Networks,2016,84:57-66.
[15]周航星,陈松灿.有序判别典型相关分析[J].软件学报,2014,25(9):2018-2025.(Zhou Hangxing,Chen Songcan.Ordinal Discriminative Canonical Correlation Analysis[J].Journal of Software,2014,25(9):2018-2025.)
[16]Hotelling H.Relations Between 2 Sets of Variants[J].Biometrika,1935,28(3-4):312-377.
[17]Yoshida K,Yoshimoto J,Doya K.Sparse Kernel Canonical Correlation Analysis for Discovery of Nonlinear Interactions in High-dimensional Data[J].BMC Bioinformatics,2017,18(1):108-118.
[18]钟敏娟,万常选,刘德喜.基于关联规则挖掘和极性分析的商品评论情感词典构建[J].情报学报,2016,35(5):501-509.(Zhong Minjuan,Wan Changxuan,Liu Dexi.Opinion Lexicon Construction Based on Association Rule and Orientation Analysis for Production Review[J].Journal of the China Society for Scientific and Technical Information,2016,35(5):501-509.)
[19]刘德喜.情感词扩展对微博情感分类性能影响的实验分析[J].小型微型计算机系统,2016,37(5):957-965.(Liu Dexi.Effect of Sentimental Word Expansion on the Performance of Microblog Sentiment Classification Task[J].Journal of Chinese Computer Systems,2016,37(5):957-965.)
[20]阳林.情感词权值研究及在情感极性分析中的应用[J].计算机应用,2015,35(S2):125-127.(Yang Lin.Emotional Term Weight Research and Application to Emotional Polarity Analysis[J].Journal of Computer Applications,2015,35(S2):125-127.)
[21]Van Arthur G,Staals F,L?ffler M,et al.Multi-Granular Trend Detection for Time-Series Analysis[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(1):661-670.
[22]唐晓波,童海燕,严承希.基于话题情感强度的微博舆情分析[J].图书馆学研究,2014(17):85-93.(Tang Xiaobo,Tong Haiyan,Yan Chengxi.Microblogging Public Opinion Analysis Based on Emotional Intensity of the Topic[J].Research on Libray Science,2014(17):85-93.)
[23]Refaee E,Rieser V.i Lab-Edinburgh at Sem Eval-2016 Task 7:A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases[C]//Proceedings of the 10th International Workshop on Semantic Evaluation.2016.