Topic Modeling Toolkit

Lin, Jiayue; Pang, Mingkai; Liu, Yulong

Topic Modeling Toolkit

dc.contributor.author	Lin, Jiayue	en
dc.contributor.author	Pang, Mingkai	en
dc.contributor.author	Liu, Yulong	en
dc.date.accessioned	2023-07-05T17:43:28Z	en
dc.date.available	2023-07-05T17:43:28Z	en
dc.date.issued	2023-05-08	en
dc.description.abstract	The Topic Modeling Toolkit project began with an existing text mining toolkit and aimed to enhance its functionality by incorporating cutting-edge topic modeling techniques. Specifically, BERTopic, CTM, and LDA were used to extract pertinent topics from a corpus of text documents. The resulting web-based platform provides users with a search engine, a recommendation system, and a usable interface for browsing and exploring these topics. In addition to these enhancements, our team also implemented a text-filtering framework and redesigned the user interface using Tailwind CSS. The final deliverables of the project include a fully functional website, user documentation, and an open-source toolkit that can be used to train machine learning models and support browsing and searching for various text datasets. While the current version of the toolkit includes BERTopic, CTM, and LDA, there is potential for future work to incorporate additional topic modeling methods. It is important to note that while the project originally focused on electronic theses and dissertations (ETDs), the resulting platform can be used to explore and comprehend complex subjects within any corpus of text documents. The topic modeling toolkit is available as an open-source package that users can install and use on their own computers. It is available for use and can be used to support browsing and searching for various text datasets. The intended user group for the platform includes researchers, students, and other users interested in exploring and understanding complex topics within a given corpus of text documents. The resulting topic modeling toolkit offers features that facilitate the exploration and comprehension of intricate topics within text document collections. This tool has the potential to aid researchers, students, and other users in their respective fields.	en
dc.description.notes	TopicModelingToolkitReport.pdf - Final report as PDF file TopicModelingToolkitReport.zip - Final report downloaded from Overleaf TopicModelingToolkitFinalPres.pdf - Final Presentation as PDF file TopicModelingToolkitFinalPres.pptx - Final Presentation as pptx file	en
dc.identifier.uri	http://hdl.handle.net/10919/115648	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.rights	Attribution 4.0 International	en
dc.rights	Attribution 4.0 International	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.subject	Machine learning	en
dc.subject	Topic modeling	en
dc.subject	User interface design	en
dc.subject	Text filtering	en
dc.subject	Tailwind CSS	en
dc.subject	BERTopic	en
dc.subject	Search algorithms	en
dc.subject	Recommendation algorithms	en
dc.title	Topic Modeling Toolkit	en
dc.type	Presentation	en
dc.type	Report	en