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Automated Generation of Latent Topics on Emerging Technologies from YouTube Video Content

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Title:Automated Generation of Latent Topics on Emerging Technologies from YouTube Video Content
Authors:Daniel, Clinton
Dutta, Kaushik
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Social Media Analytics, Topic Modeling, Machine Learning, YouTube
Date Issued:03 Jan 2018
Abstract:Topic modeling has been widely adopted by researchers for a variety of different research problems that involve the mining of text corpora to generate a latent set of topics. Specifically, the Latent Dirichlet Allocation (LDA) algorithm is well documented within academic literature in terms of its application and automated topic generation from data sources such as blogs, social media, and other text collections. YouTube now offers access to over a billion auto-generated video transcript documents that have been recorded and posted to its social platform. The availability of this data offers an opportunity for researchers to investigate a variety of topics that are being discussed and posted to the platform. Specifically, we will study, using the LDA algorithm, discussions related to emerging technologies that have been posted on YouTube to better understand what latent topics can be auto-generated and what kind of methodology can be used to analyze this data.
Pages/Duration:9 pages
URI/DOI:http://hdl.handle.net/10125/50109
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.222
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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