dc.contributor.author	He, Chongyu	en
dc.contributor.author	Wei, Jianchi	en
dc.contributor.author	Mao, Chenyu	en
dc.date.accessioned	2022-05-10T19:18:46Z	en
dc.date.available	2022-05-10T19:18:46Z	en
dc.date.issued	2022-05-10	en
dc.description.abstract	Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful in a wide range of research areas. Accordingly, we are building electronic infrastructure leveraging advanced work on digital libraries, for discovering and accessing the knowledge buried in ETDs. We focus on our work to incorporate topic modeling into digital libraries for ETDs. We present ETD-Topics, a framework that extracts topics from a large text corpus in an unsupervised way. The representations learnt from topic models can be useful for downstream tasks such as searching and/or browsing documents by topic, document recommendation, topic recommendation, and describing temporal topic trends (e.g., from the perspective of disciplines or universities). The characteristics of different models make the classification distinguished. We provide four modes (LDA, NeuralLDA, ProdLDA, and CTM) to serve user groups with different browsing and searching requirements. Our job was to import the preprocessed database and the trained models (four models with different topic numbers), and to accurately display key information (such as topics, document title, abstract, etc.) on web pages. We chose Python as the main language to implement the back-end, while using Flask as a bridge connecting the back-end and front-end. On the basis of using HTML for displaying data, we were able to use JavaScript and CSS to make the whole set of web pages look more fluent and comfortable by optimizing the UI, to include graphic bars, buttons (like “Submit”, “Show more”, etc.), and tables.	en
dc.description.notes	TextMiningReport.pdf is the full report of the project as PDF file, including introduction, design, implementation, testing, manual, and lessons learned. TextMiningReport.docx is the full report of the project with .docx format, including introduction, design, implementation, testing, manual, and lessons learned. TextMiningPresentation.pdf is the final presentation slides as PDF file, including project review, timeline, video demo, and future work. TextMiningPresentation.pptx is the final presentation slides with .pptx format, including project review, timeline, video demo, and future work. TextMiningDemo.mp4 is a website demo with .mp4 format, including index page, topic page, document page, advanced research page, and search keyword page. ETD_Topic_Models_5page.pdf is a pre-print of a conference paper submission as a PDF file.	en
dc.identifier.uri	http://hdl.handle.net/10919/109986	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	en
dc.subject	TextMining	en
dc.subject	ETDs	en
dc.subject	documents	en
dc.subject	LDA	en
dc.subject	NeuralLDA	en
dc.subject	ProdLDA	en
dc.subject	CTM	en
dc.subject	Topic modeling	en
dc.title	TextMining	en
dc.type	Presentation	en
dc.type	Report	en
dc.type	Video	en

TextMining

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