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Text Classification and Topic Modeling for Online Discussion Forums: An Empirical Study From the Systems Modeling Community

Text Classification and Topic Modeling for Online Discussion Forums: An Empirical Study From the Systems Modeling Community
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Author(s): Xin Zhao (University of Alabama, USA), Zhe Jiang (University of Alabama, USA) and Jeff Gray (The University of Alabama, USA)
Copyright: 2020
Pages: 36
Source title: Trends and Applications of Text Summarization Techniques
Source Author(s)/Editor(s): Alessandro Fiori (Candiolo Cancer Institute – FPO, IRCCS, Italy)
DOI: 10.4018/978-1-5225-9373-7.ch006

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Abstract

Online discussion forums play an important role in building and sharing domain knowledge. An extensive amount of information can be found in online forums, covering every aspect of life and professional discourse. This chapter introduces the application of supervised and unsupervised machine learning techniques to analyze forum questions. This chapter starts with supervised machine learning techniques to classify forum posts into pre-defined topic categories. As a supporting technique, web scraping is also discussed to gather data from an online forum. After this, this chapter introduces unsupervised learning techniques to identify latent topics in documents. The combination of supervised and unsupervised machine learning approaches offers us deeper insights of the data obtained from online forums. This chapter demonstrates these techniques through a case study on a very large online discussion forum called LabVIEW from the systems modeling community. In the end, the authors list future trends in applying machine learning to understand the expertise captured in online expert communities.

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