Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000193-6.doi: 10.11896/jsjkx.211000193

• Artificial Intelligence • Previous Articles     Next Articles

Study on Evolution of Sentiment-Topic of Internet Reviews with Time in Emergencies

SHI Wei1, FU Yue2   

  1. 1 School of Economics and Management,Huzhou Normal University,Huzhou,Zhejiang 313000,China
    2 School of Economics and Management,Huzhou University,Huzhou,Zhejiang 313000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:SHI Wei,born in 1981,Ph.D,professor.His main research interests include business intelligence and affective computing.
  • Supported by:
    General program of National Social Science Foundation of China(20BXW013).

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Abstract: The analysis of sentiment topic evolution is of great value to the control of network public opinion in emergencies.According to the dynamic characteristics of sentiment topics,this paper constructs a sentiment topic model based on LDA,analyzes the evolution of sentiment topics with time through the joint modeling of time,topic and sentiment,deduces the reasoning algorithm based on Gibbs sampling process,and finally puts forward the analysis results of product reviews and microblog emergency data sets,which shows that the joint model has good accuracy and good applicability in the process of time evolution.

Key words: Time-aware sentiment-topic model(TST), Time series, Trend analysis, Sentiment analysis

CLC Number: 

  • TP391.1
[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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