Data mining and association rules to determine twitter trends

Opinion mining has been widely studied in the last decade due to its great interest in the field of research and countless real-world applications. This research proposes a system that combines association rules, generalization of rules, and sentiment analysis to catalog and discover opinion trends...

Full description

Autores:
Silva, Jesús
Vargas, Jesús
Natteri, Domingo
Flores Marín, Darío Enrique
Pineda, Omar
Ahumada, Bridy
Valero, Lesbia
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7735
Acceso en línea:
https://hdl.handle.net/11323/7735
https://doi.org/10.1007/978-981-15-4875-8_23
https://repositorio.cuc.edu.co/
Palabra clave:
Opinions mining
Association rules
Sentiment analysis
Analysis of trends
Unsupervised learning
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Data mining and association rules to determine twitter trends
title Data mining and association rules to determine twitter trends
spellingShingle Data mining and association rules to determine twitter trends
Opinions mining
Association rules
Sentiment analysis
Analysis of trends
Unsupervised learning
title_short Data mining and association rules to determine twitter trends
title_full Data mining and association rules to determine twitter trends
title_fullStr Data mining and association rules to determine twitter trends
title_full_unstemmed Data mining and association rules to determine twitter trends
title_sort Data mining and association rules to determine twitter trends
dc.creator.fl_str_mv Silva, Jesús
Vargas, Jesús
Natteri, Domingo
Flores Marín, Darío Enrique
Pineda, Omar
Ahumada, Bridy
Valero, Lesbia
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Vargas, Jesús
Natteri, Domingo
Flores Marín, Darío Enrique
Pineda, Omar
Ahumada, Bridy
Valero, Lesbia
dc.subject.spa.fl_str_mv Opinions mining
Association rules
Sentiment analysis
Analysis of trends
Unsupervised learning
topic Opinions mining
Association rules
Sentiment analysis
Analysis of trends
Unsupervised learning
description Opinion mining has been widely studied in the last decade due to its great interest in the field of research and countless real-world applications. This research proposes a system that combines association rules, generalization of rules, and sentiment analysis to catalog and discover opinion trends in Twitter [1]. The sentiment analysis is used to favor the generalization of the association rules. In this sense, an initial set of 1.6 million tweets captured in an undirected way is first summarized through text mining in an input set for the algorithms of rules and sentiment analysis of 158,354 tweets. On this last group, easily interpretable standard and generalized sets of rules are obtained about characters, which were revealed as an interesting result of the system.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-20T20:34:38Z
dc.date.available.none.fl_str_mv 2021-01-20T20:34:38Z
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/10.1007/978-981-15-4875-8_23
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)
2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)
3. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
4. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)
5. Yuan, M., Ouyang, Y., Xiong, Z., Sheng, H.: Sentiment classification of web review using association rules. In: Ozok, A.A., Zaphiris, P. (eds.) Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol 8029. Springer, Berlin, Heidelberg (2013)
6. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Discov. 4 (2–3), pp. 163–192 (2000)
7. Amelec, Viloria, Carmen, Vasquez: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015)
8. Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)
9. Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018)
10. Cagliero, L., Fiori, A.: Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic Rules. Behavior Computing: Modeling, Analysis, Mining and Decision. Springer, pp. 267–287 (2012)
11. Erlandsson, F., Bro´dka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18, 164 (2016)
12. Meduru, M., Mahimkar, A., Subramanian, K., Padiya, P.Y., Gunjgur, N.: Opinion mining using Twitter feeds for political analysis. Int. J. Comput. (IJC) 25(1), 116–123 (2017)
13. Abascal-Mena, R., Lo´pez-Ornelas, E., Zepeda-Herna´ndez, J.S.: User generated content: an analysis of user behavior by mining political tweets. In: Ozok A.A., Zaphiris, P. (eds.). Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol 8029. Springer, Berlin, Heidelberg (2013)
14. Dehkharghani, R., Mercan, H., Javeed, A., Saygin, Y.: Sentimental causal rule discovery from Twitter. Expert Syst. Appl. 41(10), 4950–4958 (2014)
15. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs Sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363–370 (2005)
16. Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
17. Torres Samuel, M., Vásquez, C., Viloria, A., Hernández Fernandez, L., Portillo Medina, y.R.: Analysis of Patterns in the university Word Rankings Webometrics, Shangai, QS and SIRScimago: Case Latin American.  Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligent and Lecture Notes in Bioinformatics) (2018)
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spelling Silva, JesúsVargas, JesúsNatteri, DomingoFlores Marín, Darío EnriquePineda, OmarAhumada, BridyValero, Lesbia2021-01-20T20:34:38Z2021-01-20T20:34:38Z2020https://hdl.handle.net/11323/7735https://doi.org/10.1007/978-981-15-4875-8_23Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Opinion mining has been widely studied in the last decade due to its great interest in the field of research and countless real-world applications. This research proposes a system that combines association rules, generalization of rules, and sentiment analysis to catalog and discover opinion trends in Twitter [1]. The sentiment analysis is used to favor the generalization of the association rules. In this sense, an initial set of 1.6 million tweets captured in an undirected way is first summarized through text mining in an input set for the algorithms of rules and sentiment analysis of 158,354 tweets. On this last group, easily interpretable standard and generalized sets of rules are obtained about characters, which were revealed as an interesting result of the system.Silva, JesúsVargas, JesúsNatteri, DomingoFlores Marín, Darío Enrique-will be generated-orcid-0000-0003-4180-8463-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Ahumada, BridyValero, Lesbiaapplication/pdfengCorporación 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_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_23Opinions miningAssociation rulesSentiment analysisAnalysis of trendsUnsupervised learningData mining and association rules to determine twitter trendsArtículo 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. Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)3. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)4. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)5. Yuan, M., Ouyang, Y., Xiong, Z., Sheng, H.: Sentiment classification of web review using association rules. In: Ozok, A.A., Zaphiris, P. (eds.) Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol 8029. Springer, Berlin, Heidelberg (2013)6. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Discov. 4 (2–3), pp. 163–192 (2000)7. Amelec, Viloria, Carmen, Vasquez: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015)8. Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)9. Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018)10. Cagliero, L., Fiori, A.: Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic Rules. Behavior Computing: Modeling, Analysis, Mining and Decision. Springer, pp. 267–287 (2012)11. Erlandsson, F., Bro´dka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18, 164 (2016)12. Meduru, M., Mahimkar, A., Subramanian, K., Padiya, P.Y., Gunjgur, N.: Opinion mining using Twitter feeds for political analysis. Int. J. Comput. (IJC) 25(1), 116–123 (2017)13. Abascal-Mena, R., Lo´pez-Ornelas, E., Zepeda-Herna´ndez, J.S.: User generated content: an analysis of user behavior by mining political tweets. In: Ozok A.A., Zaphiris, P. (eds.). Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol 8029. Springer, Berlin, Heidelberg (2013)14. Dehkharghani, R., Mercan, H., Javeed, A., Saygin, Y.: Sentimental causal rule discovery from Twitter. Expert Syst. Appl. 41(10), 4950–4958 (2014)15. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs Sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363–370 (2005)16. Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)17. Torres Samuel, M., Vásquez, C., Viloria, A., Hernández Fernandez, L., Portillo Medina, y.R.: Analysis of Patterns in the university Word Rankings Webometrics, Shangai, QS and SIRScimago: Case Latin American.  Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligent and Lecture Notes in Bioinformatics) (2018)PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/db057e7f-f4e1-415b-8e46-f0d13a597d92/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALData mining and association rules to determine twitter trends.pdfData mining and association rules to determine twitter trends.pdfapplication/pdf100012https://repositorio.cuc.edu.co/bitstreams/85ab1f4e-8528-424c-8f62-8ccb3297e5dc/download3aed22ff8076319785848a6191dd6ae1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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