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...

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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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oai_identifier_str oai:repositorio.cuc.edu.co:11323/7699
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv 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
Twitter
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
title_full_unstemmed Classification, identification, and analysis of events on twitter through data mining
title_sort Classification, identification, and analysis of events on twitter through data mining
dc.creator.fl_str_mv 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
Twitter
Opinion mining
Classification
topic Machine learning
Twitter
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.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-01-15T18:12:41Z
dc.date.available.none.fl_str_mv 2021-01-15T18:12:41Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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https://doi.org/10.1007/978-981-15-7234-0_89
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
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dc.relation.references.spa.fl_str_mv 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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spelling Silva, JesúsHiga, YukiCera Visbal, Juan ManuelCabrera, DanelysSenior Naveda, AlexaFlores, YasminPineda Lezama, Omar Bonerge2021-01-15T18:12:41Z2021-01-15T18:12:41Z2021https://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.Silva, JesúsHiga, YukiCera Visbal, Juan ManuelCabrera, DanelysSenior Naveda, AlexaFlores, YasminPineda Lezama, Omar Bonergeapplication/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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