An approach for discovering keywords from Spanish tweets using Wikipedia

• AYALA, Daniel ^[1] ; ROLDÁN, Juan C. ^[2] ; RUIZ, David ^[2] ; GALLEGO, Fernando O. ^[2]
  1. [1] Universidad de Sevilla

     Universidad de Sevilla

     Sevilla, España

     
  2. [2] University of Sevilla
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 4, Nº. 2, 2015, págs. 73-88
• Idioma: inglés
• DOI: 10.14201/ADCAIJ2015427388
• Enlaces
  • Texto completo
• Resumen

  • Most approaches to keywords discovery when analyzing microblogging messages (among them those from Twitter) are based on statistical and lexical information about the words that compose the text. The lack of context in the short messages can be problematic due to the low co-occurrence of words. In this paper, we present a new approach for keywords discovering from Spanish tweets based on the addition of context information using Wikipedia as a knowledge base. We present four different ways to use Wikipedia and two ways to rank the new keywords. We have tested these strategies using more than 60000 Spanish tweets, measuring performance and analyzing particularities of each strategy.

• Referencias bibliográficas
  • Blei, D. M., 2012. Probabilistic topic models. Communications of the ACM, 55(4):77–84.
  • http://dx.doi.org/10.1145/2133806.2133826
  • Chen, Y., Li, Z., Nie, L., Hu, X., Wang, X., Chua, T.-s., and Zhang, X., 2012. A Semi-Supervised Bayesian Network Model for Microblog Topic...
  • Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., and Ghosh, R., 2013. Discovering coherent topics
  • http://dx.doi.org/10.1145/2505515.2505519
  • Dubhashi, D. P. and Panconesi, A., 2009. Concentration of measure for the analysis of randomized algorithms. Cambridge University Press.
  • http://dx.doi.org/10.1017/CBO9780511581274
  • Hennig-Thurau, T., Wiertz, C., and Feldhaus, F., 2014. Does Twitter matter? The impact of microblogging word of mouth on consumers' adoption...
  • http://dx.doi.org/10.1007/s11747-014-0388-3
  • Hu, X. and Liu, H., 2012. Text analytics in social media. In Mining text data, pages 385–414. Springer.
  • http://dx.doi.org/10.1007/978-1-4614-3223-4_12
  • Hulpus, I., Hayes, C., Karnstedt, M., and Greene, D., 2013. Unsupervised graph-based topic labelling using dbpedia. In Proceedings of the...
  • http://dx.doi.org/10.1145/2433396.2433454
  • Ko, Y., 2012. A study of term weighting schemes using class information for text classification. In Proceedings
  • http://dx.doi.org/10.1145/2348283.2348453
  • Nenkova, A. and McKeown, K., 2012. A survey of text summarization techniques. In Mining Text Data, pages 43–76. Springer.
  • http://dx.doi.org/10.1007/978-1-4614-3223-4_3
  • Ren, F. and Sohrab, M. G., 2013. Class-indexing-based term weighting for automatic text classification. Information Sciences, 236:109–125.
  • http://dx.doi.org/10.1016/j.ins.2013.02.029
  • Thorleuchter, D. and Van den Poel, D., 2012. Improved multilevel security with latent semantic indexing. Expert Systems with Applications,...
  • http://dx.doi.org/10.1016/j.eswa.2012.06.002
  • Thorleuchter, D. and Van den Poel, D., 2013. Technology classification with latent semantic indexing. Expert
  • Systems with Applications, 40(5):1786–1795.
  • Xie, J., Emenheiser, J., Kirby, M., Sreenivasan, S., Szymanski, B., Holme, P. et al., 2012. Evolution of
  • Yubo Chen, S. F. and Wang, Q., 2011. The Role of Marketing in Social Media: How Online Consumer Reviews Evolve. Journal of Interactive Marketing,...
  • http://dx.doi.org/10.1016/j.intmar.2011.01.003
  • Zhang, W., Yoshida, T., and Tang, X., 2011. A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Systems with...
  • http://dx.doi.org/10.1016/j.eswa.2010.08.066
  • Zhu, J., Chen, N., Perkins, H., and Zhang, B., 2014. Gibbs max-margin topic models with data augmentation.