Abstract
Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company.
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References
Twitter web site (2013), http://twitter.com
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)
Bruckhaus, T.: Collective intelligence in marketing. In: Casillas, J., Martínez-López, F.J. (eds.) Marketing Intelligent Systems Using Soft Computing. STUDFUZZ, vol. 258, pp. 131–154. Springer, Heidelberg (2010)
Buhmann, M.D.: Radial Basis Functions. Cambridge University Press, New York (2003)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
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)
Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29(2-3), 103–130 (1997)
Frakes, W.B., Baeza-Yates, R.A. (eds.): Information Retrieval: Data Structures & Algorithms. Prentice-Hall (1992)
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)
Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, 9th edn. Wiley-Interscience (March 1990)
Larose, D.T.: Discovering Knowledge in Data. John Wiley and Sons (2005)
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)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
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)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
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)
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)
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Bello, G., Menéndez, H., Okazaki, S., Camacho, D. (2013). Extracting Collective Trends from Twitter Using Social-Based Data Mining. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_62
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DOI: https://doi.org/10.1007/978-3-642-40495-5_62
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