國立虎尾科技大學 |

Model for Analyzing Course Description Using LDA Topic Modeling.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Model for Analyzing Course Description Using LDA Topic Modeling./
作者:	Kallem, Snehith Reddy.
面頁冊數:	1 online resource (40 pages)
附註:	Source: Masters Abstracts International, Volume: 84-08.
Contained By:	Masters Abstracts International84-08.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798368463056

Model for Analyzing Course Description Using LDA Topic Modeling.
Kallem, Snehith Reddy.

Model for Analyzing Course Description Using LDA Topic Modeling. - 1 online resource (40 pages)

Source: Masters Abstracts International, Volume: 84-08.

Thesis (M.S.)--The University of North Carolina at Greensboro, 2022.

Includes bibliographical references

This study demonstrates a way to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA, which is relatively significant. This model will specifically be able to differentiate the course structure between various universities, such as the University of North Carolina at Wilmington, the University of North Texas, the University of South Carolina, and the University of Western Carolina. This model will help find the related courses of a selected department of study, or so they thought. The LDA (Latent Dirichlet Allocation) topic model is used to infer topics from the content in the university course description. Further, this study showed how to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA. This study will: Explain how to Infer topics from the corpora consisting of various universities' text of course details; Helps to find out the related courses of a selected department of study in a big way; Group the topics into different communities by calculating the Modularity with the help of the Louvain method; Analyze how the courses are related to the topics, for the most part subtly inferred for each University; For a selected Department of study, see what all courses belongs to this department with the help of topics generated. This study helps us to identify the courses which have a relation with a selected department of study. The graph representations mainly included in this paper will generally explain our Approach.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798368463056Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

AnalyzeIndex Terms--Genre/Form:

554714
Electronic books.

Model for Analyzing Course Description Using LDA Topic Modeling.
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500 $a Advisor: Mohanty, Somya D.
502 $a Thesis (M.S.)--The University of North Carolina at Greensboro, 2022.
504 $a Includes bibliographical references
520 $a This study demonstrates a way to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA, which is relatively significant. This model will specifically be able to differentiate the course structure between various universities, such as the University of North Carolina at Wilmington, the University of North Texas, the University of South Carolina, and the University of Western Carolina. This model will help find the related courses of a selected department of study, or so they thought. The LDA (Latent Dirichlet Allocation) topic model is used to infer topics from the content in the university course description. Further, this study showed how to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA. This study will: Explain how to Infer topics from the corpora consisting of various universities' text of course details; Helps to find out the related courses of a selected department of study in a big way; Group the topics into different communities by calculating the Modularity with the help of the Louvain method; Analyze how the courses are related to the topics, for the most part subtly inferred for each University; For a selected Department of study, see what all courses belongs to this department with the help of topics generated. This study helps us to identify the courses which have a relation with a selected department of study. The graph representations mainly included in this paper will generally explain our Approach.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
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