计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 270-273.doi: 10.11896/j.issn.1002-137X.2017.08.046
王振飞,刘凯莉,郑志蕴,王飞
WANG Zhen-fei, LIU Kai-li, ZHENG Zhi-yun and WANG Fei
摘要: 话题演化研究有助于追踪用户的喜好和话题的发展趋势,对于舆情预警具有重要意义。目前,话题演化方法注重运用话题生成模型实现话题演化分析,忽略了话题中时间因素和背景词的存在。以传统话题生成模型LDA为基础,将其扩展为微博话题生成模型MTLDA。MTLDA模型增加了对背景词的考虑,提高了话题生成的效率,同时对微博话题集进行时间片划分,利用KL距离计算相邻时间片话题距离,分析话题演化情况。以新浪微博数据为例进行实验,结果表明,MTLDA模型通过时间片划分完成了微博话题的生成,话题演化结果与实际情况吻合。
[1] REN L,DU Y,MA S.Visual Analytics Toward Big Data[J].Journal of Software,2014,25(9):1909-1936.(in Chinese) 任磊,杜一,马帅.大数据可视分析综述[J].软件学报,2014,5(9):1909-1936. [2] XU J,WANG G Y,YU H.Review of Big Data Processing Based on Granular Compu-ting[J].Chinese Journal of Computers,2015,38(8):1497-1517.(in Chinese) 徐计,王国胤,于洪.基于粒计算的大数据处理[J].计算机学报,2015,38(8):1497-1517. [3] ZHAO X J,YANG C M,LI B.A Topic Evolution Minning Algorithm of News Text Based on Feature Evolving[J].Chinese Journal of Computers,2014(4):819-832.(in Chinese) 赵旭剑,杨春明,李波.一种基于特征演变的新闻话题演化挖掘方法[J].计算机学报,2014(4):819-832. [4] CUI K,ZHOU B,JIA Y.LDA-based Model for Online Topic Evolution Mining[J].Computer Science,2010,37(11):156-193.(in Chinese) 崔凯,周斌,贾焰.一种基于LDA的在线主题演化挖掘模型[J].计算机科学,2010,37(11):156-193. [5] HU Y L,BAI L,ZHANG W M.Modeling and Analyzing Topic Evolution[J].Acta Automatica Sinica,2012,38(10):1690-1697.(in Chinese) 胡艳丽,白亮,张维明.一种话题演化建模与分析方法[J].自动化学报,2012,38(10):1690-1697. [6] FANG Y,HUANG H Y,XIN X.Topic Evolutionary Analysisfor Dynamic Topic Number[J].Journal of Chinese Information Processing,2014,28(3):142-149.(in Chinese) 方莹,黄河燕,辛欣.面向动态主题数的话题演化分析[J].中文信息学报,2014,28(3):142-149. [7] XU W,ZHAO B,JI G L.Microblog Topic Evolution Algorithm Based on Retweeti-ng Relationship[J].Computer Science,2016,3(2):79-100.(in Chinese) 徐伟,赵斌,吉根林.基于转发关系的微博话题演化算法[J].计算机科学,2016,3(2):79-100. [8] JAYASHRI M,CHITRA P.Topic Clustering and Topic Evolution Based On Temporal Parameters[C]∥International Confe-rence on Recent Trends in Information Technology.Chennai,India:IEEE,2012:559-564. [9] JENSEN S,LIU X Z,YU Y G.Generation of topic evolution trees from heterogeneous bibliographic networks[J].Journal of Informetrics,2016,4(2):606-621. [10] JO Y,HOPCROFT J E,LAGOZE C.The Web of Topics:Discovering the Topology of Topic Evolution in a Corpus[C]∥WWW 2011-Session:Spatio-Temporal Analysis.Hyderabad,India:ACM,2011:257-266. [11] ZHAO A H,LIU P U,ZHENG Y.Subtopic Division in News Topic Based on Latent Dirichlet Allocation[J].Journal of Chinese Computer Systems,2013,4(4):732-737.(in Chinese) 赵爱华,刘培玉,郑燕.基于LDA的新闻话题子话题划分方法[J].小型微型计算机系统,2013,34(4):732-737. [12] DING Z Y,ZHOU B,JIA Y.Detecting Spammers with a Bidirectional Vote Algorithm Based on Statistical Features in Microblogs[J].Journal of Computer Research and Development,2013,0(11):2336-2348.(in Chinese) 丁兆云,周斌,贾焰.微博中基于统计特征与双向投票的垃圾用户发现[J].计算机研究与发展,2013,0(11):2336-2348. [13] CAI G Y,PENG L B,WANG Y.Topic Detection and Evolution Analysis on Microblog[C]∥International Federation for Information Processing.Trondheim,Norway:2014:67-77. [14] ZAHO B,XU W,JI G L.Discovering Topic Evolution Topology in a Microblog Corpus [C]∥Third International Conference on Advanced Cloud and Big Data.YangZhou,JiangSu,China:CBD,2016:7-14. [15] BLEI D M,NG A Y,JORDAN M I.Latent Dirichlet Allocation[J].The Journal of Machine Learning Research,2003,3(3):993-1022. [16] CAO J P,WANG H,XIA Y Q.Bi-path Evolution Model for Online Topic Model Based on LDA[J].Acta Automatica Sinica,2014,40(12):2877-2886.(in Chinese) 曹建平,王晖,夏友清.基于LDA的双通道在线主题演化模型[J].自动化学报,2014,40(12):2877-2886. |
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