Results 201 to 210 of about 1,143,739 (216)

Using Topic Modeling to Understand Patients' and Caregivers' Perspectives About Left Ventricular Assist Device: Thematic Analysis.

J Med Internet Res
Melnikov S, Klein S, Shahar M, Guy D.
europepmc   +1 more source

Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences.

J Med Internet Res
Walsh J, Cave J, Griffiths F.
europepmc   +1 more source

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

J Med Internet Res
Rao VK, Valdez D, Muralidharan R, Agley J, Eddens KS, Dendukuri A, Panth V, Parker MA.   +7 more
europepmc   +1 more source

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study.

J Med Internet Res
Correia JC, Ahmad SS, Waqas A, Meraj H, Pataky Z.   +4 more
europepmc   +1 more source

Dynamic topic models [PDF]

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
David M. Blei, John Lafferty
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Topic model validation

Neurocomputing, 2012
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Ramirez, E, Brena, R, Magatti, D, STELLA, FABIO ANTONIO   +3 more
openaire   +3 more sources

Topic structure modeling

Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002
In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (classifier) creation.
Victor M. Sheftel, James G. Shanahan, David A. Evans   +2 more
openaire   +2 more sources

Topic-OPA: A Topic Ontology for Modeling Topics of Old Press Articles

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2020
International ...
El Ghosh, Mirna, Zanni-Merk, Cecilia, Delestre, Nicolas, Kotowicz, Jean-Philippe, Abdulrab, Habib   +4 more
openaire   +5 more sources