Extracting collective trends from Twitter using social-based data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8083 LNAI, Issue , 2013, Pages 622-630

  Bello, Gema ^a   Menéndez, Héctor ^a   Okazaki, Shintaro ^a   Camacho, David ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

Classification; Clustering; Collective trends; Data mining; Social network; Twitter

Indexed keywords

CLASSIFICATION AND CLUSTERING; CLUSTERING; COLLECTIVE TRENDS; KNOWLEDGE EXTRACTION; SHORT TEXTS; SOCIAL-BASED; TEXT MINING TECHNIQUES; TWITTER;

CLASSIFICATION (OF INFORMATION); EXTRACTION; SOCIAL NETWORKING (ONLINE);

DATA MINING;

EID: 84892910853     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40495-5_62     Document Type: Conference Paper

References (19)

1. Twitter web site (2013), http://twitter.com
2. Ahonen-Myka, H.: Mining all maximal frequent word sequences in a set of sentences. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, pp. 255-256. ACM, New York (2005)
3. Bruckhaus, T.: Collective intelligence in marketing. In: Casillas, J., Martínez-López, F.J. (eds.) Marketing Intelligent Systems Using Soft Computing. STUD-FUZZ, vol. 258, pp. 131-154. Springer, Heidelberg (2010)
4. Buhmann, M.D.: Radial Basis Functions. Cambridge University Press, New York (2003)
5. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273-297 (1995)
6. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21-27 (1967)
7. Cutting, D.R., Karger, D.R., Pedersen, J.O., Tukey, J.W.: Scatter/gather: a cluster-based approach to browsing large document collections. In: Proceedings of the 15th Annual International ACM Sigir Conference on Research and Development in Information Retrieval, SIGIR 1992, pp. 318-329. ACM, New York (1992)
8. Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29(2-3), 103-130 (1997)
9. Frakes, W.B., Baeza-Yates, R.A. (eds.): Information Retrieval: Data Structures & Algorithms. Prentice-Hall (1992)
10. Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)
11. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100-108 (1979)
12. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, 9th edn. Wiley-Interscience (March 1990)
13. Larose, D.T.: Discovering Knowledge in Data. John Wiley and Sons (2005)
14. Li, Y., Chung, S.M., Holt, J.D.: Text document clustering based on frequent word meaning sequences. Data Knowl. Eng. 64(1), 381-404 (2008)
15. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
16. Powers, D.M.W.: Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Technical Report SIE-07-001, School of Informatics and Engineering, Flinders University, Adelaide, Australia (2007)
17. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
18. Trung, D.N., Jung, J.J., Lee, N., Kim, J.: Thematic analysis by discovering diffusion patterns in social media: An exploratory study with tweetScope. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part II. LNCS, vol. 7803, pp. 266-274. Springer, Heidelberg (2013)
19. Zamir, O., Etzioni, O.: Web document clustering: a feasibility demonstration. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 46-54. ACM, New York (1998)