Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41171

Towards a Sentiment Analyzing Discussion-board

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dc.contributor.author Thoms, Brian
dc.contributor.author Eryilmaz, Evren
dc.contributor.author Mercado, Glo
dc.contributor.author Ramirez, Benjamin
dc.contributor.author Rodriguez, Jose
dc.date.accessioned 2016-12-29T00:10:00Z
dc.date.available 2016-12-29T00:10:00Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41171
dc.description.abstract In this paper we present the design and construction of a sentiment analyzing discussion board, which was used to support learning and interaction within an existing online social networking (OSN) system. More specifically, this research introduces an innovative extension to learning management software (LMS) that combines real-time sentiment analysis with the goal of fostering student engagement and course community. In this study we perform data mining to extract sentiment on over 6,000 historical discussion board posts. This initial data was analyzed for sentiment and interaction patterns and used for guiding the redesign of an existing asynchronous online discussion board (AOD). The redesign incorporates a sentiment analyzer, which allows users to analyze the sentiment of their individual contributions prior to submission. Preliminary results found that the proposed system produced more favorable outcomes when compared to existing AOD software.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject asynchronous online discussion
dc.subject data mining
dc.subject design science research
dc.subject online social networking
dc.subject sentiment analysis
dc.title Towards a Sentiment Analyzing Discussion-board
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.021
Appears in Collections: Advances in Teaching and Learning Minitrack


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