Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

William Deitrick, Wei Hu

Department of Computer Science, Houghton College, Houghton, USA.

DOI: 10.4236/jdaip.2013.13004                

Abstract

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.

Keywords

Community Detection; Sentiment Analysis; Twitter; Online Social Networks; Modularity; Community-Level Sentiment Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. H. Wang, “Don’t Follow Me: Spam Detection in Twitter,” Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), Piraeus, 26-28 July 2010, pp. 1-10.
[2]	T. Falkowski, A. Barth and M. Spiliopoulou, “Studying Community Dynamics with an Incremental Graph Mining Algorithm,” Proceedings of the 14th Americas Conference on Information Systems (AMCIS 2008), Toronto, 14-17 August 2008, pp. 1-11.
[3]	K. Liu, W. Li and M. Guo, “Emoticon Smoothed Language Models for Twitter Sentiment Analysis,” Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, 22-26 July 2012, pp. 1678-1684.
[4]	A. Burns and B. Eltham, “Twitter Free Iran: An Evaluation of Twitter’s Role in Public Diplomacy and Information Operations in Iran’s 2009 Election Crisis,” Record of the Communications Policy and Research Forum 2009, Sydney, 19-20 November 2009, pp. 322-334.
[5]	D. Gayo-Avello, P. T. Metaxas and E. Mustafaraj, “Limits of Electoral Predictions Using Twitter,” Proceedings of the International Conference on Weblogs and Social Media (ICWSM) 2011, Barcelona, 17-21 July 2011, pp. 490-493.
[6]	B. J. Jansen, M. Zhang, K. Sobel and A. Chowdury, “Twitter Power: Tweets as Electronic Word of Mouth,” Journal of the American Society for Information and Technology, Vol. 60, No. 11, 2009, pp. 2169-2188. doi:10.1002/asi.21149
[7]	M. van Meeteren, A. Poorthuis and E. Dugundji, “Mapping Communities in Large Virtual Social Networks,” Proceedings of the 1st International Forum on the Application and Management of Personal Electronic Information, Cambridge, 12-13 October 2009, 8 Pages.
[8]	S. E. Schaeffer, “Graph Clustering,” Computer Science Review, Vol. 1, No. 1, 2007, pp. 27-64. doi:10.1016/j.cosrev.2007.05.001
[9]	M. Girvan and M. E. J. Newman, “Community Structure in Social and Biological Networks,” Proceedings of the National Academy of the Sciences of the United States of America, Vol. 99, No. 12, 2002, pp. 7821-7826. doi:10.1073/pnas.122653799
[10]	I. X. Y. Leung, P. Hui, P. Lio and J. Crowcroft, “Towards Real-Time Community Detection in Large Networks,” Physical Review E, Vol. 79, No. 6, 2009, Article ID: 066107. doi:10.1103/PhysRevE.79.066107
[11]	T. Falkowski, A. Barth and M. Spilioupoulou, “Dengraph: A Density-Based Community Detection Algorithm,” Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, 2-5 November 2007, pp. 113-115.
[12]	M. E. J. Newman and M. Girvan, “Finding and Evaluating Community Structure in Networks,” Physical Review E, Vol. 69, No. 2, 2004, Article ID: 026113. doi:10.1103/PhysRevE.69.026113
[13]	S. Fortunato and M. Barthelemy, “Resolution Limit in Community Detection,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 104, No. 1, 2007, pp. 36-41. doi:10.1073/pnas.0605965104
[14]	B. H. Good, Y. de Montjoye and A. Clauset, “Performance of Modularity Maximization in Practical Contexts,” Physical Review E, Vol. 81, No. 4, 2010, Article ID: 046106. doi:10.1103/PhysRevE.81.046106
[15]	A. Lancichinetti, F. Radicchi, J. J. Ramasco and S. Fortunato, “Finding Statistically Significant Communities in Networks,” PLoS ONE, Vol. 6, No. 4, 2011, Article ID: e18961. doi:10.1371/journal.pone.0018961
[16]	A. Lancichinetti, S. Fortunato and J. Kertész, “Detecting the Overlapping and Hierarchical Community Structure in Complex Networks,” New Journal of Physics, Vol. 11, No. 3, 2009, Article ID: 033015. doi:10.1088/1367-2630/11/3/033015
[17]	M. Newman, “Networks: An Introduction,” 1st Edition, Oxford University Press, Inc., New York, 2010.
[18]	J. Xie, “Agent-Based Dynamics Models for Opinion Spreading and Community Detection in Large-Scale Social Networks,” Ph.D. Thesis, Rensselaer Polytechnic Institute, Troy, 2012.
[19]	M. Rosvall and C. T. Bergstrom, “Maps of Random Walks on Complex Networks Reveal Community Structure,” Proceedings of the National Academy of the Sciences of the United States of America, Vol. 105, No. 4, 2008, pp. 1118-1123. doi:10.1073/pnas.0706851105
[20]	A. Esuli and F. Sebastiani, “SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining,” Proceedings of the 5th Conference on Language Resources and Evaluation, Genoa, 24-26 May 2006, pp. 417-422.
[21]	A. Hamouda and M. Rohaim, “Reviews Classification Using SentiWordNet Lexicon,” The Online Journal on Computer Science and Information Technology, Vol. 2, No. 1, 2011, pp. 120-123.
[22]	F. Chaumartin, “UPAR7: A Knowledge-Based System for Headline Sentiment Tagging,” Proceedings of the 4th International Workshop on Semantic Evaluations, Prague, 23-24 June 2007, pp. 422-425. doi:10.3115/1621474.1621568
[23]	K. Denecke, “Using SentiWordNet for Multilingual Sentiment Analysis,” Proceedings of the IEEE 24th International Conference on Data Engineering Workshop, Cancún, 7-12 April 2008, pp. 507-512.
[24]	B. Ohana and B. Tierney, “Sentiment Classification of Reviews Using SentiWordNet,” Proceedings of the 9th IT&T Conference, Dublin, 22-23 October 2009, 9 Pages.
[25]	A. Pak and P. Paroubek, “Twitter As a Corpus for Sentiment Analysis and Opinion Mining,” Proceedings of the International Conference on Language Resources and Evaluation, Malta, 19-21 May 2010, pp. 1320-1326.
[26]	M. Speriosu, N. Sudan, S. Upadhyay and J. Baldridge, “Twitter Polarity Classification with Label Propagation Over Lexical Links and the Follower Graph,” Proceedings of the 1st Workshop on Unsupervised Learning in NLP, Edinburgh, 30 July 2011, pp. 53-63.