Comment classification for an online news domain
Date
2014-12
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Abstract
ENGLISH ABSTRACT: In online discussion forums, comment moderation systems are often faced with the problem of establishing the value of an unseen online comment. By knowing the value of comments, the system is empowered to establish rank and to
enhance the user experience. It is also useful for identifying malicious users that consistently show behaviour that is detrimental to the community.
In this paper, we investigate and evaluate various machine
learning techniques for automatic comment scoring. We derive
a set of features that aim to capture various comment
quality metrics (like relevance, informativeness and spelling)
and compare it to content-based features. We investigate the
correlation of these features against the community popularity
of the comments. Through investigation of supervised
learning techniques, we show that content-based features
better serves as a predictor of popularity, while quality-based
features are better suited for predicting user engagement.
We also evaluate how well our classifier based rankings correlate
to community preference.
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar
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Description
Please cite as follows:
Brand, D. & Van Der Merwe, B. 2014. Comment classification for an online news domain, in Proceedings of the First International Conference on the use of Mobile Informations and Communication Technology (ICT) in Africa UMICTA 2014, 9-10 December 2014, STIAS Conference Centre, Stellenbosch: Stellenbosch University, Department of Electrical & Electronic Engineering, South Africa, ISBN: 978-0-7972-1533-7.
The conference is available at http://mtn.sun.ac.za/conference2014/
See also the record http://hdl.handle.net/10019.1/95703
Brand, D. & Van Der Merwe, B. 2014. Comment classification for an online news domain, in Proceedings of the First International Conference on the use of Mobile Informations and Communication Technology (ICT) in Africa UMICTA 2014, 9-10 December 2014, STIAS Conference Centre, Stellenbosch: Stellenbosch University, Department of Electrical & Electronic Engineering, South Africa, ISBN: 978-0-7972-1533-7.
The conference is available at http://mtn.sun.ac.za/conference2014/
See also the record http://hdl.handle.net/10019.1/95703
Keywords
Automatic comment scoring, Machine learning, Cybermetrics, Supervised learning (Machine learning)