Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis-Reference-Cited by-同舟云学术

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Published:2022-10-26 Issue:6 Volume:56 Page:5133-5260
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Murshed Belal Abdullah Hezam^ORCID,Mallappa Suresha,Abawajy Jemal^ORCID,Saif Mufeed Ahmed Naji^ORCID,Al-ariki Hasib Daowd Esmail^ORCID,Abdulwahab Hudhaifa Mohammed^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10254-w.pdf

Reference291 articles.

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3. Abou-Of MA (2020) A fuzzy, incremental and semantic trending topic detection in social feeds. In: 2020 11th international conference on information and communication systems (ICICS). IEEE, pp 118–24

4. Ahmed A, Aly M, Gonzalez J, Narayanamurthy S, Smola AJ (2012) Scalable inference in latent variable models. In: Proceedings of the fifth ACM international conference on Web search and data mining—WSDM ’12. ACM Press, New York, pp 123–32

5. Aiello LM, Petkos G, Martin C, Corney D, Papadopoulos S, Skraba R, Goker A, Kompatsiaris I, Jaimes A (2013) Sensing trending topics in Twitter. IEEE Trans Multimed 15(6):1268–1282. https://doi.org/10.1109/TMM.2013.2265080

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