Classification of Medical Dataset Along with Topic Modeling Using LDA

2018 ◽  
pp. 1-11 ◽  
Author(s):  
M. Selvi ◽  
K. Thangaramya ◽  
M. S. Saranya ◽  
K. Kulothungan ◽  
S. Ganapathy ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Medical Dataset

Application of the LDA Model to Semantic Annotation of Web-based English Educational Resources

2021 ◽  
Author(s):  
Wei Du ◽  
Haiyan Zhu ◽  
Teeraporn Saeheaw
Keyword(s):  
Topic Modeling ◽  
Basic Education ◽  
Semantic Annotation ◽  
Experimental Results ◽  
Educational Resources ◽  
Metadata Standard ◽  
Web Based ◽  
Teaching Content ◽  
Macroscopic Classification

Based on the LDA model, this paper builds a three-layer semantic model of Web English educational resources “document-topic-keyword”, models the semantic topics of resource documents, and obtains the semantic topics and keywords of document resources as the semantic labels of resources. The experimental results show that document LDA topic modeling is beneficial to the macroscopic classification of Web English educational resources. The experimental results show that LDA topic modeling of documents is useful for macroscopic cataloging of Web English educational resources, highlighting teaching priorities, difficulties, and interrelationships, while LDA modeling of teaching topics with the same teaching content expands the metadata generation method of resource description based on the basic education metadata standard and provides more information about the inherent characteristics of resources. The semantic information can be used to mine the semantic thematic features and detailed differences inherent in the resources, and the final performance analysis verifies the parallel computing advantages of the LDA model in a big data environment.

scholarly journals Probabilistic topic modeling for the analysis and classification of genomic sequences

2015 ◽  
Vol 16 (S6) ◽  
Author(s):  
Massimo La Rosa ◽  
Antonino Fiannaca ◽  
Riccardo Rizzo ◽  
Alfonso Urso
Keyword(s):  
Topic Modeling ◽  
Genomic Sequences ◽  
Probabilistic Topic Modeling

scholarly journals Classification of negative publication in mass media using topic modeling

2021 ◽  
Vol 1727 ◽  
pp. 012019
Author(s):  
Kirill Yakunin ◽  
Ravil Mukhamediev ◽  
Yan Kuchin ◽  
Rustam Musabayev ◽  
Timur Buldybayev ◽  
...  
Keyword(s):  
Mass Media ◽  
Topic Modeling

scholarly journals Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling

2017 ◽  
Vol 26 (7) ◽  
pp. 675-693 ◽  
Author(s):  
Ana Catarina Calheiros ◽  
Sérgio Moro ◽  
Paulo Rita
Keyword(s):  
Topic Modeling ◽  
Online Reviews ◽  
Sentiment Classification

A Turkish Topic Modeling Dataset For Multi-label Classification of Movie Genre

2020 ◽  
Author(s):  
Elgun Jabrayilzade ◽  
Algin Poyraz Arslan ◽  
Hasan Para ◽  
Ozan Polatbilek ◽  
Erhan Sezerer ◽  
...  
Keyword(s):  
Topic Modeling

scholarly journals Topic Modeling based on Louvain method in Online Social Networks

2016 ◽  
Author(s):  
Guilherme Sakaji Kido ◽  
Rodrigo Augusto Igawa ◽  
Sylvio Barbon Jr.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Topic Modeling ◽  
Textual Content

Online Social Networks (OSNs) are the most used media nowadays, such as Twitter. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside huge volume of data from several themes, topics and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for Topic Modeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph architecture (density and degree) allows the classification of a topic as natural or artificial, this last created by the spammers on OSNs.

Sentiment Classification of Game Review Data Using Topic Modeling

2021 ◽  
Vol 22 (9) ◽  
pp. 1477-1486
Author(s):  
Tae-Kook Kim ◽  
Kilhwan Kim
Keyword(s):  
Topic Modeling ◽  
Sentiment Classification
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