Classification, identification, and analysis of events on twitter through data mining
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 (Go...
- Autores:
-
Silva, Jesús
Higa, Yuki
Cera Visbal, Juan Manuel
Cabrera, Danelys
Senior Naveda, Alexa
Flores, Yasmin
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7699
- Acceso en línea:
- https://hdl.handle.net/11323/7699
https://doi.org/10.1007/978-981-15-7234-0_89
https://repositorio.cuc.edu.co/
- Palabra clave:
- Machine learning
Twitter
Opinion mining
Classification
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Classification, identification, and analysis of events on twitter through data mining |
| title |
Classification, identification, and analysis of events on twitter through data mining |
| spellingShingle |
Classification, identification, and analysis of events on twitter through data mining Machine learning Opinion mining Classification |
| title_short |
Classification, identification, and analysis of events on twitter through data mining |
| title_full |
Classification, identification, and analysis of events on twitter through data mining |
| title_fullStr |
Classification, identification, and analysis of events on twitter through data mining |
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Classification, identification, and analysis of events on twitter through data mining |
| title_sort |
Classification, identification, and analysis of events on twitter through data mining |
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Silva, Jesús Higa, Yuki Cera Visbal, Juan Manuel Cabrera, Danelys Senior Naveda, Alexa Flores, Yasmin Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Higa, Yuki Cera Visbal, Juan Manuel Cabrera, Danelys Senior Naveda, Alexa Flores, Yasmin Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Machine learning Opinion mining Classification |
| topic |
Machine learning Opinion mining Classification |
| description |
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. |
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2021 |
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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 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 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 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 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 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 9. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24–38 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 11. Toro EM, Mejia DA, Salazar H (2004) Pronóstico de ventas usando redes neuronales. Scientia et technica 10(26):12–25 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 13. Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146 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 15. Choudhury A, Jones J (2014) Crop yield prediction using time series models. J Econ Econ Educ Res 15:53–68 16. Scheffer T (2004) Finding association rules that trade support optimally against confidence. Intell Data Anal 9(4):381–395 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 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 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 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 |
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Silva, Jesúse17281d02925301aa71681ad0d7b3e03Higa, Yuki5155aade6f71e715b173e348acb0dbdeCera Visbal, Juan Manuel653cadb79a7a71280227189f8ceea890Cabrera, Danelys1f9f790aa17165bd0e99ba6d950da3eeSenior Naveda, Alexaab2ae139a36464b9c2fd3f8950e6a080Flores, Yasmind676ee7fab96639229eb53b6c7f28439Pineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bd2021-01-15T18:12:41Z2021-01-15T18:12:41Z2021https://hdl.handle.net/11323/7699https://doi.org/10.1007/978-981-15-7234-0_89Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.application/pdfspaCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_89Machine learningTwitterOpinion miningClassificationClassification, identification, and analysis of events on twitter through data miningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. 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, Turkey2. 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)90125–73. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–834. 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 105–1125. 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, Cham6. 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 338–3497. 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/v9i47/1073718. Villada F, Muñoz N, García E (2012) Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica 23(4):11–209. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24–3810. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–120611. Toro EM, Mejia DA, Salazar H (2004) Pronóstico de ventas usando redes neuronales. Scientia et technica 10(26):12–2512. 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 382–38713. Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–14614. 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):601–61815. Choudhury A, Jones J (2014) Crop yield prediction using time series models. J Econ Econ Educ Res 15:53–6816. Scheffer T (2004) Finding association rules that trade support optimally against confidence. Intell Data Anal 9(4):381–39517. 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 563318. 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, Cham19. 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 initiative20. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. 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