Detecting User Emotions in Twitter through Collective Classification

İbrahim İleri, Pinar Karagoz

Abstract

The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this study, a different user-centric approach for emotion detection is considered such that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement emotion inference task in social networks. Employing Twitter as a source for experimental data and working with the proposed collective classification algorithm, emotions of the users are predicted in a collaborative setting.

References

  1. Akba, F., Uc¸an, A., Sezer, E. A., and Sever, H. (2014). Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In 8th European Conference on Data Mining 2014.
  2. Akin, A. A. and Akin, M. D. (2007). Zemberek, an open source nlp framework for turkic languages. Structure, 10:1-5.
  3. Boynukalin, Z. (2012). Emotion analysis of turkish texts by using machine learning methods. Master's thesis, Middle East Technical University.
  4. Chakrabarti, S., Dom, B., and Indyk, P. (1998). Enhanced hypertext categorization using hyperlinks. In ACM SIGMOD Record, volume 27, pages 307-318. ACM.
  5. Demirci, S. (2014). Emotion analysis on turkish tweets. Master's thesis, Middle East Technical University.
  6. Ekman, P. (1999). Facial expressions. Handbook of cognition and emotion, 16:301-320.
  7. Go, A., Bhayani, R., and Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1:12.
  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1):10-18.
  9. Kent, J. T. (1983). Information gain and a general measure of correlation. Biometrika, 70(1):163-173.
  10. Kivran-Swaine, F. and Naaman, M. (2011). Network properties and social sharing of emotions in social awareness streams. In Proceedings of the ACM 2011 conference on Computer supported cooperative work, pages 379-382. ACM.
  11. Kozareva, Z., Navarro, B., Vázquez, S., and Montoyo, A. (2007). Ua-zbsa: a headline emotion classification through web information. In Proceedings of the 4th International Workshop on Semantic Evaluations, pages 334-337. Association for Computational Linguistics.
  12. Lu, Q. and Getoor, L. (2003). Link-based classification. In ICML, volume 3, pages 496-503.
  13. Macskassy, S. A. and Provost, F. (2007). Classification in networked data: A toolkit and a univariate case study. The Journal of Machine Learning Research, 8:935- 983.
  14. McDowell, L. K. and Aha, D. W. (2013). Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 847-852. ACM.
  15. Mohammad, S. M. (2012). # emotional tweets. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pages 246-255. Association for Computational Linguistics.
  16. Neville, J. and Jensen, D. (2000). Iterative classification in relational data. In Proc. AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pages 13-20.
  17. Nigam, K., McCallum, A. K., Thrun, S., and Mitchell, T. (2000). Text classification from labeled and unlabeled documents using em. Machine learning, 39(2-3):103- 134.
  18. Pang, B. and Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics, page 271. Association for Computational Linguistics.
  19. Perlich, C. and Provost, F. (2003). Aggregation-based feature invention and relational concept classes. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 167-176. ACM.
  20. Rabelo, J. C., Prudeˆncio, R. B., Barros, F., et al. (2012). Using link structure to infer opinions in social networks. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on, pages 681-685. IEEE.
  21. Tocoglu, M. A. and Alpkocak, A. (2014). Emotion extraction from turkish text. In Network Intelligence Conference (ENIC), 2014 European, pages 130-133. IEEE.
Download


Paper Citation


in Harvard Style

İleri İ. and Karagoz P. (2016). Detecting User Emotions in Twitter through Collective Classification . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 205-212. DOI: 10.5220/0006037502050212


in Bibtex Style

@conference{kdir16,
author={İbrahim İleri and Pinar Karagoz},
title={Detecting User Emotions in Twitter through Collective Classification},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={205-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006037502050212},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Detecting User Emotions in Twitter through Collective Classification
SN - 978-989-758-203-5
AU - İleri İ.
AU - Karagoz P.
PY - 2016
SP - 205
EP - 212
DO - 10.5220/0006037502050212