计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211000193-6.doi: 10.11896/jsjkx.211000193
史伟1, 付月2
SHI Wei1, FU Yue2
摘要： 网络评论的情感主题演变分析对突发事件中网络舆情的控制极具价值。针对情感主题动态性的特点,构建一个基于LDA的情感主题模型,通过对时间与主题和情感的联合建模来分析情感主题随时间的演变,推导了基于Gibbs抽样过程的推理算法,最后通过微博突发事件数据集的分析结果显示了联合模型较高的准确性和情感主题随时间演变过程中良好的应用性。
中图分类号:
| [1]LI F,HUANG M,ZHU X.Sentiment analysis with global topics and local dependency[C]//AAAI’10.2020:1371-137. [2]LIN C,HE Y,EVERSON R,et al.Weakly Supervised JointSentiment-Topic Detection from Text [J].TKDE,2012,24(6):1134-1145. [3]XU Y M,LI Y,LIANG Y,et al.Topic-sentiment evolution overtime:a manifold learning-based model for online news [J].Journal of Intelligent Information Systems,2020,55:27-49. [4]KALARANI P,BRUNDA S S.Sentiment analysis by POS and joint sentiment topic features using SVM and ANN[J].Soft Computing,2019,23:7067-7079. [5]HE Y L,LIN C H,GAO W,et al.Dynamic joint sentiment-topic model[J].Acm Transactions on Intelligent Systems & Techno-logy,2014,12(1):1-21. [6]GHOORCHIAN K,SAHLGREN M.GDTM:Graph-based Dy-namic Topic Models [J].Progress in Artificial Intelligence,2020,9:195-207. [7]GRIFFITHS T L,STEYVERS M.Finding scientific topics[J].Proceedings of the National Academy of Sciences,2004(101):5228-5235. [8]CHANG C H,MONSELISE M,YANG C C.What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter[J].Journal of Healthcare Informatics Research,2021,5:70-97. [9]LI W B,MATSUKAWA T,SAIGO H.Context-Aware Latent Dirichlet Allocation for Topic Segmentation[J].Advances in Knowledge Discovery and Data Mining,2020(5):475-486. [10]HUANG L,TAN W N,SUN Y.Collaborative recommendation algorithm based on probabilistic matrix factorization in probabilistic latent semantic analysis [J].Multimed Tools Appl,2019,78:8711-8722. [11]FATEMI M,SAFAYANI M.Joint sentiment/topic modeling on text data using a boosted restricted Boltzmann Machine [J].Multimedia Tools and Applications,2019,78:20637-20653. [12]XU Y J,SUN C H,LIU Y Z.Joint sentiment/topic model integrating user characteristics[J].Journal of Computer Applications,2018(5):1261-1266. [13]JIANG C Q,LV X Z,DUAN R.Analyzing topic evolution of online product forum based on topic model[J].Journal of Systems Engineering,2019(10):598-609. [14]XU Y M,LI Y,LIANG Y,et al.Topic-sentiment evolution over time:a manifold learning-based model for online news[J].Journal of Intelligent Information Systems,2020,55:27-49. [15]ZHU X X,SONG J X,MENG J F.Analysis of OnlinePublic Opinion Information Based on the Dynamic Theme emotion Evolution Model[J].Information Science,2019(7):72-78. [16]HE Y,LIN C,GAO W,et al.Dynamic Joint Sentiment-Topic model[J].TIST,2014(9):212-225. [17]HE Y,LIN C,GAO W,et al.Tracking Sentiment and Topic Dynamics from Social Media[C]//ICWSM’12.Dublin,Ireland:AAAI,2012:483-486. [18]WANG X,MOHANTY N,MCCALLUM A.Group and topic discovery from relations and text[C]//LinkKDD’05.Chicago,IL,USA:ACM,2005:28-35. [19]HEINRICH G.Parameter estimation for text analysis[J].Tech.Rep.,2005. [20]SHI W,WANG H W,HE S Y.Study on Construction of Fuzzy Emotion Ontology Based on HowNet[J].Journal of The China Society for Scientific Andtechnical Information,2012(6):595-602. [21]BIGI B.Using Kullback-Leibler Distance for Text Categoriza-tion[C]//ECIR’03.Pisa,Italy:Springer-Verlag,2003:305-319. [22]MINKA T P.Estimating a Dirichlet distribution[J].MIT,Tech.Rep.8,2003. |
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