Big data and automatic detection of topics: social network texts
This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in so...
- Autores:
-
Silva, Jesús
Hernandez Palma, Hugo Gaspar
Niebles Núñez, William
Ruiz Lázaro, Alex
Varela, Noel
- 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:11323/6192
- Acceso en línea:
- https://hdl.handle.net/11323/6192
https://repositorio.cuc.edu.co/
- Palabra clave:
- Big Data
Automatic detection
Social network
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Big data and automatic detection of topics: social network texts |
| title |
Big data and automatic detection of topics: social network texts |
| spellingShingle |
Big data and automatic detection of topics: social network texts Big Data Automatic detection Social network |
| title_short |
Big data and automatic detection of topics: social network texts |
| title_full |
Big data and automatic detection of topics: social network texts |
| title_fullStr |
Big data and automatic detection of topics: social network texts |
| title_full_unstemmed |
Big data and automatic detection of topics: social network texts |
| title_sort |
Big data and automatic detection of topics: social network texts |
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Silva, Jesús Hernandez Palma, Hugo Gaspar Niebles Núñez, William Ruiz Lázaro, Alex Varela, Noel |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Hernandez Palma, Hugo Gaspar Niebles Núñez, William Ruiz Lázaro, Alex Varela, Noel |
| dc.subject.spa.fl_str_mv |
Big Data Automatic detection Social network |
| topic |
Big Data Automatic detection Social network |
| description |
This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts. |
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2020 |
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2020-04-15T17:10:28Z |
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2020-04-15T17:10:28Z |
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2020 |
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eng |
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[1] A. Gonzalez-Agirre, E. Laparra, y G. Laparra, “Multilingual central repository version 3.0,” in Proceedings of the Eight International Con- ference on Language Resources and Evaluation (LREC’12). Istanbul, Turkey: European Language Resources Association (ELRA), may 2012 [2] P. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, no. 1, pp. 53–65, Nov. 1987. [Online]. Disponible: http://dx.doi.org/10.1016/0377- 0427(87)90125-7. [3] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80–83, 1945. [4] K. Toutanova, D. Klein, C. D. Manning, y Y. Singer, “Feature-rich part- of-speech tagging with a cyclic dependency network,” in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, ser. NAACL ’03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 173–180. [Online]. Disponible: http://dx.doi.org/10.3115/1073445.1073478. [5] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., PortilloMedina 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] W. X. Zhao, J. Weng, J. He, E.-P. Lim, y H. Yan, “Comparing twitter and traditional media using topic models,” in 33rd European conference on advances in information retrieval (ECIR11). Berlin, Heidelberg: Springer-Verlag., 2011, 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 Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371. [8] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.. [9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. [10] N. Swanson y H. White, “Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models”, International Journal of Forecasting, vol. 13, núm. 4, pp. 439–461, 1997. [11] E. M. Toro, D. A. Mejia, y H. Salazar, “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004. [12] Hernández, J. A., Burlak, G., Muñoz Arteaga, J., y 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 (pp. 382-387). México: Universidad Autónoma de México. [13] Romero, C., Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146. [14] Romero, C., y Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618. Disponible en: http://ieeexplore.ieee.org/xpl/RecentIssue. jsp?reload=true&punumber=5326 [15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014. [16] Scheffer, T. (2004). Finding Association Rules that Trade Support Optimally Against Confidence. Intelligent Data Analysis, 9(4), 381-395. [16] Ruß G. 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. [17] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (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 [18] Y. Rao, Q. Li, X. Mao, y L. Wenyin, “Sentiment topic models for social emotion mining,” Information Sciences, vol. 266, pp. 90 – 100, 2014. [Online]. Disponible: http://www.sciencedirect.com/science/article/pii/ S002002551400019X [19] K. Gutiérrez-Batista, J. R. Campaña, M.-A. Vila, y M. J. Martin- Bautista, “An ontology-based framework for automatic topic detection in multilingual environments,” International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1459–1475, 2018. [Online]. Disponible: https://onlinelibrary.wiley.com/doi/abs/10.1002/int.21986 [20] J. Wu, W. Gao, B. Zhang, J. Liu, y C. Li, “Cluster based detection and analysis of internet topics,” in 4th International Symposium on Computational Intelligence and Design, ISCID 2011, vol. 2, 2011, pp. 371–374. [21] L. Zheng y T. Li, “Semi-supervised hierarchical clustering,” in Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ser. ICDM ’11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 982–991. [Online]. Disponible: http://dx.doi.org/10.1109/ICDM.2011.130 [22] C. Lin y Y. He, “Joint sentiment/topic model for sentiment analysis,” in 18th ACM Conference on Information and Knowledge Management 8CIKM09). New York, NY, USA: ACM, 2009, pp. 375–384. [23] J. Duan y J. Zeng, “Web objectionable text content detection using topic modeling technique,” Expert Systems with Applications, vol. 40, pp. 6094–6104., 2013. [24] M. Pennacchiotti y S. Gurumurthy, “Investigating topic models for social media user recommendation,” in 20th International Conference Companion on World Wide Web. New York, NY, USA: ACM, 2011, pp. 101–102. |
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Silva, JesúsHernandez Palma, Hugo GasparNiebles Núñez, WilliamRuiz Lázaro, AlexVarela, Noel2020-04-15T17:10:28Z2020-04-15T17:10:28Z20201742-65881742-6596https://hdl.handle.net/11323/6192doi:10.1088/1742-6596/1432/1/012073Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.Silva, JesúsHernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Niebles Núñez, WilliamRuiz Lázaro, Alex-will be generated-orcid-0000-0002-5974-2864-600Varela, NoelengJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Big DataAutomatic detectionSocial networkBig data and automatic detection of topics: social network textsArtículo 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/acceptedVersion[1] A. Gonzalez-Agirre, E. Laparra, y G. Laparra, “Multilingual central repository version 3.0,” in Proceedings of the Eight International Con- ference on Language Resources and Evaluation (LREC’12). Istanbul, Turkey: European Language Resources Association (ELRA), may 2012[2] P. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, no. 1, pp. 53–65, Nov. 1987. [Online]. Disponible: http://dx.doi.org/10.1016/0377- 0427(87)90125-7.[3] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80–83, 1945.[4] K. Toutanova, D. Klein, C. D. Manning, y Y. Singer, “Feature-rich part- of-speech tagging with a cyclic dependency network,” in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, ser. NAACL ’03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 173–180. [Online]. Disponible: http://dx.doi.org/10.3115/1073445.1073478.[5] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., PortilloMedina 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] W. X. Zhao, J. Weng, J. He, E.-P. Lim, y H. Yan, “Comparing twitter and traditional media using topic models,” in 33rd European conference on advances in information retrieval (ECIR11). Berlin, Heidelberg: Springer-Verlag., 2011, 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 Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371.[8] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012..[9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.[10] N. Swanson y H. White, “Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models”, International Journal of Forecasting, vol. 13, núm. 4, pp. 439–461, 1997.[11] E. M. Toro, D. A. Mejia, y H. Salazar, “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004.[12] Hernández, J. A., Burlak, G., Muñoz Arteaga, J., y 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 (pp. 382-387). México: Universidad Autónoma de México.[13] Romero, C., Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.[14] Romero, C., y Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618. Disponible en: http://ieeexplore.ieee.org/xpl/RecentIssue. jsp?reload=true&punumber=5326[15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014.[16] Scheffer, T. (2004). Finding Association Rules that Trade Support Optimally Against Confidence. Intelligent Data Analysis, 9(4), 381-395.[16] Ruß G. 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.[17] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (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[18] Y. Rao, Q. Li, X. Mao, y L. Wenyin, “Sentiment topic models for social emotion mining,” Information Sciences, vol. 266, pp. 90 – 100, 2014. [Online]. Disponible: http://www.sciencedirect.com/science/article/pii/ S002002551400019X[19] K. Gutiérrez-Batista, J. R. Campaña, M.-A. Vila, y M. J. Martin- Bautista, “An ontology-based framework for automatic topic detection in multilingual environments,” International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1459–1475, 2018. [Online]. Disponible: https://onlinelibrary.wiley.com/doi/abs/10.1002/int.21986[20] J. Wu, W. Gao, B. Zhang, J. Liu, y C. Li, “Cluster based detection and analysis of internet topics,” in 4th International Symposium on Computational Intelligence and Design, ISCID 2011, vol. 2, 2011, pp. 371–374.[21] L. Zheng y T. Li, “Semi-supervised hierarchical clustering,” in Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ser. ICDM ’11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 982–991. [Online]. Disponible: http://dx.doi.org/10.1109/ICDM.2011.130[22] C. Lin y Y. He, “Joint sentiment/topic model for sentiment analysis,” in 18th ACM Conference on Information and Knowledge Management 8CIKM09). New York, NY, USA: ACM, 2009, pp. 375–384.[23] J. Duan y J. Zeng, “Web objectionable text content detection using topic modeling technique,” Expert Systems with Applications, vol. 40, pp. 6094–6104., 2013.[24] M. Pennacchiotti y S. 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