Computational methods for text mining user posts on a popular gaming forum for identifying user experience issues

The advent of the social web such as twitter, facebook and the numerous social forums have provided a rich source of data representing human beliefs, social interactions and opinions that can be analysed. In this paper we show how extracting user sentiment by text mining posts from popular gaming forums can be used to identify user experience problems and issues that can adversely effect the enjoyment and gaming experience for the customers. The users posts are downloaded, preprocessed and parsed, we label the posts as negative, positive or neutral in terms of sentiment. We then identify key areas for game play improvement based on the frequency counts of keywords and key phrases used by the fora members. Furthermore, computational models based on complex network theory can rank the issues and provide knowledge about the relationships between them.

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Author and article information

Contributors

Ken McGarry

Sharon McDonald

Conference

Publication date: July 2017

Publication date (Print): July 2017

Pages: 1-7

Affiliations

[0001]University of Sunderland

Sunderland, UK

Article

DOI: 10.14236/ewic/HCI2017.100

SO-VID: c6f9968d-8b9b-42c0-a83c-ea7dd89f4b6c

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Electronic Visualisation and the Arts (EVA 2017)

Conference acronym: EVA

Conference number:

Conference location: London, UK

Conference date: 11 – 13 July 2017

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2017.100

Self URI (journal page): https://ewic.bcs.org/

reference

S. Bose 2017 RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews 259 268 Springer Singapore
I. FeinererK. HornikD. Meyer 2008 Text mining infrastructure in r Journal of Statistical Software 25 1 1 54
M. Hearst 1999 Untangling text data mining In Proceedings of ACL ’99: the 37th Annual Meeting of the Association for Computational Linguistics 126 136
A. KumarR. Jai 2015 Sentiment analysis and feedback evaluation. In in 2015 IeEe 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE) 433–436
M. MissikoffP. VelardiP. Fabriani 2003 Text mining techniques to automatically enrich a domain ontology Applied Intelligence 18 323 340
U. NahmR. Mooney 2002 Text mining with information extraction U. NahmR. Mooney Text Mining with Information Extraction In Proc. AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases.
J. Nielsen 1993 Usability Engineering Academic Press Boston, USA.
R Core Team 2015 R: A Language and Environment for Statistical Computing Vienna, Austria R Foundation for Statistical Computing.
C. RomeroS. Ventura 2010 Educational data mining: a review of the state of the art IEEE Transactions on Systems, Man and Cybernetics. Part C Appl. Rev. 40 6 601 618
UKI 2017 The games industry in numbers Association for UK Interactive Entertainment

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Comment on this article

[1] S. Bose 2017 RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews 259 268 Springer Singapore

[2] I. FeinererK. HornikD. Meyer 2008 Text mining infrastructure in r Journal of Statistical Software 25 1 1 54

[3] M. Hearst 1999 Untangling text data mining In Proceedings of ACL ’99: the 37th Annual Meeting of the Association for Computational Linguistics 126 136

[4] A. KumarR. Jai 2015 Sentiment analysis and feedback evaluation. In in 2015 IeEe 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE) 433–436

[5] M. MissikoffP. VelardiP. Fabriani 2003 Text mining techniques to automatically enrich a domain ontology Applied Intelligence 18 323 340

[6] U. NahmR. Mooney 2002 Text mining with information extraction U. NahmR. Mooney Text Mining with Information Extraction In Proc. AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases.

[7] J. Nielsen 1993 Usability Engineering Academic Press Boston, USA.

[8] R Core Team 2015 R: A Language and Environment for Statistical Computing Vienna, Austria R Foundation for Statistical Computing.

[9] C. RomeroS. Ventura 2010 Educational data mining: a review of the state of the art IEEE Transactions on Systems, Man and Cybernetics. Part C Appl. Rev. 40 6 601 618

[10] UKI 2017 The games industry in numbers Association for UK Interactive Entertainment

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