Skip to main content

Abstract

Due to its popularity, Twitter is currently one of the major players in the global network, which has established a new form of communication: the microblogging. Twitter has become an essential media network for the follow-up, diffusion and coordination of events of diverse nature and importance (Gonzalez-Agirre et al. in Multilingual central repository version 3.0. Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). Istanbul, Turkey, 2012, [1]), such as a presidential campaign, a disaster situation, a war or the repercussion of information. In such scenario, it is considered a relevant source of information to know the opinions that are emitted about different issues or people. This research proposes the evaluation of several supervised classification algorithms to address the problem of opinion mining on Twitter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gonzalez-Agirre A, Laparra E, Laparra G (2012) Multilingual central repository version 3.0. In: Proceedings of the eight international conference on language resources and evaluation (LREC’12), May 2012. European Language Resources Association (ELRA), Istanbul, Turkey

    Google Scholar 

  2. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65 [Online]. Disponible: http://dx.doi.org/10.1016/0377-0427(87)90125–7

  3. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–83

    Article  Google Scholar 

  4. Riloff E, Janyce W (2003) Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 conference on empirical methods in natural language processing, EMNLP’03. Association for Computational LinguisticsStroudsburg, PA, USA, pp 105–112

    Google Scholar 

  5. Lis-Gutiérrez JP, Gaitán-Angulo M, Henao LC, Viloria A, Aguilera-Hernández D, Portillo-Medina R (2018) Measures of concentration and stability: two pedagogical tools for industrial organization courses. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham

    Google Scholar 

  6. Zhao WX, Weng J, He J, Lim EP, Yan H (2011) Comparing twitter and traditional media using topic models. In: 33rd European conference on advances in information retrieval (ECIR11). Springer-Verlag, Berlin, Heidelberg, pp 338–349

    Google Scholar 

  7. Viloria A, Gaitan-Angulo M (2016) Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J Sci Technol 9(47). https://doi.org/10.17485/ijst/2016/v9i47/107371

  8. Villada F, Muñoz N, García E (2012) Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica 23(4):11–20

    Google Scholar 

  9. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24–38

    Google Scholar 

  10. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206

    Article  Google Scholar 

  11. Toro EM, Mejia DA, Salazar H (2004) Pronóstico de ventas usando redes neuronales. Scientia et technica 10(26):12–25

    Google Scholar 

  12. Hernández JA, Burlak G, Muñoz Arteaga J, Ochoa A (2006) Propuesta para la evaluación de objetos de aprendizaje desde una perspectiva integral usando minería de datos. En A. Hernández y J. Zechinelli (eds.) Avances en la ciencia de la computación. Universidad Autónoma de México, México, pp 382–387

    Google Scholar 

  13. Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146

    Article  Google Scholar 

  14. Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. Syst Man Cybern Part C Appl Rev IEEE Trans 40(6):601–618

    Article  Google Scholar 

  15. Choudhury A, Jones J (2014) Crop yield prediction using time series models. J Econ Econ Educ Res 15:53–68

    Google Scholar 

  16. Scheffer T (2004) Finding association rules that trade support optimally against confidence. Intell Data Anal 9(4):381–395

    Article  Google Scholar 

  17. Ruß G (2009) Data mining of agricultural yield data: a comparison of regression models. In: Perner P (eds) Advances in data mining. Applications and theoretical aspects, ICDM 2009. Lecture notes in computer science, vol 5633

    Google Scholar 

  18. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (Big Data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham

    Google Scholar 

  19. Berrocal JLA, Figuerola CG, Rodrıguez AZ (2013) Reina at RepLab2013 topic detection task: community detection. In: Proceedings of the fourth international conference of the CLEF initiative

    Google Scholar 

  20. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jesús Silva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, J. et al. (2021). Classification, Identification, and Analysis of Events on Twitter Through Data Mining. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_89

Download citation

Publish with us

Policies and ethics