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Mapping Five Years of #FOAMed: Trends, Engagement, and Shifting Topics on Twitter/X. [PDF]
West J Emerg MedGünsoy E +3 more
europepmc +1 more source
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey [PDF]
Multimedia Tools and Applications, 2018 Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc ...
Hamed Jelodar, Feng Xia, Yongli Wang
exaly +4 more sources
Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints [PDF]
Expert Systems With Applications, 2019 A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time.
Kaveh Bastani +2 more
exaly +3 more sources
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A framework of Urdu topic modeling using latent dirichlet allocation (LDA)
2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018In this age, text mining research community has given an immense attention towards the development of text mining tools, techniques and models. Topic modeling is an area of Text Mining which is being used in various areas e.g. summarization, searching, semantics, and many other.
Khadija Shakeel +3 more
openaire +1 more source
Latent Dirichlet Allocation (LDA) for Anomaly Detection in Avionics Networks
2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), 2020Latent Dirichlet Allocation (LDA) and Variational Inference are applied in near real-time to detect anomalies in ground vehicle network traffic for a ground vehicle network. The technical approach, that utilizes the Natural Language Processing (NLP) technique to detect potential malicious attacks and network configuration issues, is described and the ...
Adam Thornton +2 more
exaly +2 more sources
Journal of Information Science, 2014
AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ...
Bassam Al-Salemi +2 more
openaire +1 more source
AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ...
Bassam Al-Salemi +2 more
openaire +1 more source
Extracting Promising Topics on Smart Manufacturing Based on Latent Dirichlet Allocation (LDA)
2019 International Conference on Information and Communication Technology Convergence (ICTC), 2019Although smart manufacturing (SM) has attracted enormous attention, it is ambiguous how to realize it due to lack of practical evidence and academic knowledge on technological components. Accordingly, it is required to explore knowledge landscape to investigate promising technologies.
Young Seog Yoon +2 more
openaire +1 more source