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|a 10.1007/s10462-022-10254-w
|2 doi
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Murshed, Belal Abdullah Hezam
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Short text topic modelling approaches in the context of big data
|b taxonomy, survey, and analysis
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| 264 |
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 06.11.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling
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|a Journal Article
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4 |
|a Big data
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| 650 |
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4 |
|a Coherence
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| 650 |
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4 |
|a Data streaming
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| 650 |
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4 |
|a Deep learning topic modeling
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| 650 |
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4 |
|a Short text topic modeling
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| 650 |
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4 |
|a Social media
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| 650 |
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4 |
|a Sparseness
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| 700 |
1 |
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|a Mallappa, Suresha
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Abawajy, Jemal
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Saif, Mufeed Ahmed Naji
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Al-Ariki, Hasib Daowd Esmail
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Abdulwahab, Hudhaifa Mohammed
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Artificial intelligence review
|d 1998
|g 56(2023), 6 vom: 28., Seite 5133-5260
|w (DE-627)NLM098184490
|x 0269-2821
|7 nnas
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| 773 |
1 |
8 |
|g volume:56
|g year:2023
|g number:6
|g day:28
|g pages:5133-5260
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| 856 |
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|u http://dx.doi.org/10.1007/s10462-022-10254-w
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