Topic facet modeling: Semantic visual analytics for online discussion forums

Ihan Hsiao, Piyush Awasthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

In this paper, we propose a novel Topic Facet Model (TFM), a probabilistic topic model that assumes all words in single sentence are generated from one topic facet. The model is applied to automatically extract forum posts semantics for uncovering the content latent structures. We further prototype a visual analytics interface to present online discussion forum semantics. We hypothesize that the semantic modeling through analytics on open online discussion forums can help users examine the post content by viewing the summarized topic facets. Our preliminary results demonstrated that TFM can be a promising method to extract topic specificity from conversational and relatively short texts in online programming discussion forums.

Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery
Pages231-235
Number of pages5
Volume16-20-March-2015
ISBN (Print)9781450334174
DOIs
StatePublished - Mar 16 2015
Event5th International Conference on Learning Analytics and Knowledge, LAK 2015 - Poughkeepsie, United States
Duration: Mar 16 2015Mar 20 2015

Other

Other5th International Conference on Learning Analytics and Knowledge, LAK 2015
CountryUnited States
CityPoughkeepsie
Period3/16/153/20/15

Keywords

  • Automated Assessment
  • Discourse Analytics
  • Discussion Forums
  • LDA
  • Learning Analytics
  • Programming
  • SLDA
  • TFM
  • Topic Modeling

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Hsiao, I., & Awasthi, P. (2015). Topic facet modeling: Semantic visual analytics for online discussion forums. In ACM International Conference Proceeding Series (Vol. 16-20-March-2015, pp. 231-235). Association for Computing Machinery. https://doi.org/10.1145/2723576.2723613