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

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dc.contributor.authorDaniel, Clinton
dc.contributor.authorDutta, Kaushik
dc.date.accessioned2017-12-28T00:53:16Z
dc.date.available2017-12-28T00:53:16Z
dc.date.issued2018-01-03
dc.description.abstractTopic 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.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2018.222
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50109
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectSocial Media Analytics, Topic Modeling, Machine Learning, YouTube
dc.titleAutomated Generation of Latent Topics on Emerging Technologies from YouTube Video Content
dc.typeConference Paper
dc.type.dcmiText

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