Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano
Este proyecto de investigación explora las metodologías y tecnologías que permiten a las organizaciones utilizar datos de redes sociales como fuente de información empresarial en el contexto colombiano para apoyar decisiones estratégicas y operativas. A través de una revisión de la literatura, se es...
- Autores:
-
Pérez Rivera, Shadith
Calderón Molina, Valentina
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74868
- Acceso en línea:
- https://hdl.handle.net/1992/74868
- Palabra clave:
- Analítica de datos
Redes sociales
Fuente de información
Toma de decisiones
Extracción de datos
Procesamiento de datos
Almacenamiento de datos
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| dc.title.alternative.eng.fl_str_mv |
Business decisions through social data analytics in Colombia |
| title |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| spellingShingle |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano Analítica de datos Redes sociales Fuente de información Toma de decisiones Extracción de datos Procesamiento de datos Almacenamiento de datos Ingeniería |
| title_short |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| title_full |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| title_fullStr |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| title_full_unstemmed |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| title_sort |
Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombiano |
| dc.creator.fl_str_mv |
Pérez Rivera, Shadith Calderón Molina, Valentina |
| dc.contributor.advisor.none.fl_str_mv |
Ávila Cifuentes, Oscar Javier |
| dc.contributor.author.none.fl_str_mv |
Pérez Rivera, Shadith Calderón Molina, Valentina |
| dc.subject.keyword.spa.fl_str_mv |
Analítica de datos Redes sociales Fuente de información Toma de decisiones Extracción de datos Procesamiento de datos Almacenamiento de datos |
| topic |
Analítica de datos Redes sociales Fuente de información Toma de decisiones Extracción de datos Procesamiento de datos Almacenamiento de datos Ingeniería |
| dc.subject.themes.none.fl_str_mv |
Ingeniería |
| description |
Este proyecto de investigación explora las metodologías y tecnologías que permiten a las organizaciones utilizar datos de redes sociales como fuente de información empresarial en el contexto colombiano para apoyar decisiones estratégicas y operativas. A través de una revisión de la literatura, se establecen hipótesis sobre los beneficios y desafíos de la analítica de los datos. Estas hipótesis se validan con entrevistas a profesionales colombianos del sector, quienes aportan sus perspectivas de cómo la analítica de datos puede mejorar la toma de decisiones en áreas claves como el marketing, la gestión de productos y la logística. El proyecto detalla los procesos de extracción, procesamiento, almacenamiento y análisis de datos provenientes de plataformas digitales. A través de un marco metodológico, se evalúa el estado del arte internacional y se valida los hallazgos con profesionales de la industria colombiana. Los resultados indican que la integración de datos de redes sociales en la toma de decisiones empresariales puede mejorar la eficiencia y efectividad de las operaciones, proporcionando información valiosa en tiempo real y permitiendo una personalización más precisa de productos y servicios en el contexto colombiano. |
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2024-08-01T19:25:49Z |
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2024-07-30 |
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Trabajo de grado - Pregrado |
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M. Meiryani, S. Dewiyanti, A. D. Zahra, W. Santoso, and H. Deviarti, “Big Data Analytics in Indonesia : Literature Study,” in ACM International Conference Proceeding Series, 2023, pp. 23–28. doi: 10.1145/3603955.3603960. C.-Y. Huang, C.-L. Yang, and Y.-H. Hsiao, “A novel framework for mining social media data based on text mining, topic modeling, random forest, and danp methods,” Mathematics, vol. 9, no. 17, 2021, doi: 10.3390/math9172041. A. Jabbar, P. Akhtar, and S. Dani, “Real-time big data processing for instantaneous marketing decisions: A problematization approach,” Industrial Marketing Management, vol. 90, pp. 558–569, 2020, doi: 10.1016/j.indmarman.2019.09.001. R. Math, Big data analytics: Recent and emerging application in services industry, vol. 654. 2018. doi: 10.1007/978-981-10-6620-7_21. H. Zhang, Z. Zang, H. Zhu, M. I. Uddin, and M. A. Amin, “Big data-assisted social media analytics for business model for business decision making system competitive analysis,” Inf Process Manag, vol. 59, no. 1, 2022, doi: 10.1016/j.ipm.2021.102762. G. A. de Oliveira, R. O. Albuquerque, C. A. B. de Andrade, R. T. de Sousa, A. L. S. Orozco, and L. J. G. Villalba, “Anonymous real-time analytics monitoring solution for decision making supported by sentiment analysis,” Sensors (Switzerland), vol. 20, no. 16, pp. 1–29, 2020, doi: 10.3390/s20164557. S. A. Farimani, M. V. Jahan, and A. Milani Fard, “From Text Representation to Financial Market Prediction: A Literature Review,” Information (Switzerland), vol. 13, no. 10, 2022, doi: 10.3390/info13100466. P. Nanda and V. Kumar, “Information Processing and Data Analytics for Decision Making: A Journey from Traditional to Modern Approaches,” Information Resources Management Journal, vol. 35, no. 2, 2022, doi: 10.4018/IRMJ.291693. R. A. Camama, J. M. Baldelomar, J. M. Claveria, M. C. Espinas, J. M. Cabardo, and L. W. Rabago, “Q-DAR: Quick disaster aid and response model using Naïve Bayes and Bag-of-Words algorithm,” in IOP Conference Series: Materials Science and Engineering, 2019. doi: 10.1088/1757-899X/482/1/012051. A. S. Yüksel and F. G. Tan, “A real-time social network-based knowledge discovery system for decision making,” Automatika, vol. 59, no. 3, pp. 262–274, 2018, doi: 10.1080/00051144.2018.1531214. M. Arafeh, P. Ceravolo, A. Mourad, E. Damiani, and E. Bellini, “Ontology based recommender system using social network data,” Future Generation Computer Systems, vol. 115, pp. 769–779, 2021, doi: 10.1016/j.future.2020.09.030. S. Ahmadi, S. Shokouhyar, M. Amerioun, and N. Salehi Tabrizi, “A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks,” Journal of Retailing and Consumer Services, vol. 76, 2024, doi: 10.1016/j.jretconser.2023.103540. S. Ahmadi, S. Shokouhyar, M. H. Shahidzadeh, and I. Elpiniki Papageorgiou, “The bright side of consumers’ opinions of improving reverse logistics decisions: a social media analytic framework,” International Journal of Logistics Research and Applications, vol. 25, no. 6, pp. 977–1010, 2022, doi: 10.1080/13675567.2020.1846693. C. A. Bono, C. Cappiello, B. Pernici, E. Ramalli, and M. Vitali, “Pipeline Design for Data Preparation for Social Media Analysis,” Journal of Data and Information Quality, vol. 15, no. 4, 2023, doi: 10.1145/3597305. A. Bustamante, L. Sebastia, and E. Onaindia, “Can tourist attractions boost other activities around? A data analysis through social networks,” Sensors (Switzerland), vol. 19, no. 11, 2019, doi: 10.3390/s19112612. S. Das, A. Dutta, G. Medina, L. Minjares-Kyle, and Z. Elgart, “Extracting patterns from Twitter to promote biking,” IATSS Research, vol. 43, no. 1, pp. 51–59, 2019, doi: 10.1016/j.iatssr.2018.09.002. S. Tuarob et al., “DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction,” Financial Innovation, vol. 7, no. 1, 2021, doi: 10.1186/s40854-021-00269-7. I. Lasri, A. Riadsolh, and M. Elbelkacemi, “Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies,” International Journal of Emerging Technologies in Learning, vol. 18, no. 5, pp. 42–61, 2023, doi: 10.3991/ijet.v18i05.35959. B. Abu-Salih, P. Wongthongtham, D. Zhu, K. Y. Chan, and A. Rudra, Social Big Data Analytics: Practices, Techniques, and Applications. 2021. doi: 10.1007/978-981-33-6652-7. K. Cortis and B. Davis, “Over a decade of social opinion mining: a systematic review,” Artif Intell Rev, vol. 54, no. 7, pp. 4873–4965, 2021, doi: 10.1007/s10462-021-10030-2. C. R. Valêncio, L. M. M. Silva, W. Tenório, G. F. D. Zafalon, A. C. Colombini, and M. Z. Fortes, “Data warehouse design to support social media analysis in a big data environment,” Journal of Computer Science, vol. 16, no. 2, pp. 126–136, 2020, doi: 10.3844/JCSSP.2020.126.136. H. Mallek, F. Ghozzi, and F. Gargouri, “Conceptual modeling of big data SPJ operations with Twitter social medium,” Soc Netw Anal Min, vol. 13, no. 1, 2023, doi: 10.1007/s13278-023-01112-w. L. Dalla Valle and R. Kenett, “Social media big data integration: A new approach based on calibration,” Expert Syst Appl, vol. 111, pp. 76–90, 2018, doi: 10.1016/j.eswa.2017.12.044. S. C. K. Tékouabou, J. Chenal, R. Azmi, H. Toulni, E. B. Diop, and A. Nikiforova, “Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development,” Data (Basel), vol. 7, no. 12, 2022, doi: 10.3390/data7120170. A. Mohamed, M. K. Najafabadi, Y. B. Wah, E. A. K. Zaman, and R. 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Recupero, “Event Detection in Finance Using Hierarchical Clustering Algorithms on News and Tweets,” PeerJ Comput Sci, vol. 7, pp. 1–39, 2021, doi: 10.7717/PEERJ-CS.438. S. Giglio, F. Bertacchini, E. Bilotta, and P. Pantano, “Using social media to identify tourism attractiveness in six Italian cities,” Tour Manag, vol. 72, pp. 306–312, 2019, doi: 10.1016/j.tourman.2018.12.007. F. Polese, M. V. Ciasullo, O. Troisi, and G. Maione, “Sustainability in footwear industry: a big data analysis,” Sinergie, vol. 37, no. 1, pp. 149–170, 2019, doi: 10.7433/s108.2019.09. T. Russo Spena, M. Tregua, A. Ranieri, and F. Bifulco, Business Intelligence and Social Media Analytics. 2021. doi: 10.1007/978-3-030-63376-9_7. D. Jia, B. Yin, and X. Huang, “Social Network Big Data Hierarchical High-Quality Node Mining,” Wirel Commun Mob Comput, vol. 2021, 2021, doi: 10.1155/2021/1444755. W. G. Mutasher and A. F. Aljuboori, “New and Existing Approaches Reviewing of Big Data Analysis with Hadoop Tools,” Baghdad Science Journal, vol. 19, no. 4, pp. 887–898, 2022, doi: 10.21123/bsj.2022.19.4.0887. F. Sassite, M. Addou, and F. Barramou, “A Machine Learning and Multi-Agent Model to Automate Big Data Analytics in Smart Cities,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, pp. 441–451, 2022, doi: 10.14569/IJACSA.2022.0130754. J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, “Comparing a traditional approach for financial brand communication analysis with a big data analytics technique,” IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301. S. Shokouhyar, A. Dehkhodaei, and B. Amiri, “A mixed-method approach for modelling customer-centric mobile phone reverse logistics: application of social media data,” Journal of Modelling in Management, vol. 17, no. 2, pp. 655–696, 2022, doi: 10.1108/JM2-07-2020-0191. S. Sakr, Z. Maamar, A. Awad, B. 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Ávila Cifuentes, Oscar Javiervirtual::19541-1Pérez Rivera, ShadithCalderón Molina, Valentina2024-08-01T19:25:49Z2024-08-01T19:25:49Z2024-07-30https://hdl.handle.net/1992/74868instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Este proyecto de investigación explora las metodologías y tecnologías que permiten a las organizaciones utilizar datos de redes sociales como fuente de información empresarial en el contexto colombiano para apoyar decisiones estratégicas y operativas. A través de una revisión de la literatura, se establecen hipótesis sobre los beneficios y desafíos de la analítica de los datos. Estas hipótesis se validan con entrevistas a profesionales colombianos del sector, quienes aportan sus perspectivas de cómo la analítica de datos puede mejorar la toma de decisiones en áreas claves como el marketing, la gestión de productos y la logística. El proyecto detalla los procesos de extracción, procesamiento, almacenamiento y análisis de datos provenientes de plataformas digitales. A través de un marco metodológico, se evalúa el estado del arte internacional y se valida los hallazgos con profesionales de la industria colombiana. Los resultados indican que la integración de datos de redes sociales en la toma de decisiones empresariales puede mejorar la eficiencia y efectividad de las operaciones, proporcionando información valiosa en tiempo real y permitiendo una personalización más precisa de productos y servicios en el contexto colombiano.This research project explores the methodologies and technologies that enable organizations to use social media data as a source of business information to support strategic and operational decisions within the Colombian context. Through a literature review, hypotheses are established regarding the benefits and types of decisions supported by social data analytics. These hypotheses are validated through interviews with Colombian professionals in the sector, who provide their perspectives on how data analytics can enhance decision-making in key areas such as marketing, product management, and logistics. The results indicate that the integration of social media data into business decision-making can improve the efficiency and effectiveness of operations, providing valuable real-time information and enabling more precise customization of products and services in Colombia.Pregrado76 páginasapplication/pdfspaUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Decisiones empresariales mediante la analítica de datos en redes sociales: una aproximación al contexto colombianoBusiness decisions through social data analytics in ColombiaTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPAnalítica de datosRedes socialesFuente de informaciónToma de decisionesExtracción de datosProcesamiento de datosAlmacenamiento de datosIngenieríaM. Meiryani, S. Dewiyanti, A. D. Zahra, W. Santoso, and H. Deviarti, “Big Data Analytics in Indonesia : Literature Study,” in ACM International Conference Proceeding Series, 2023, pp. 23–28. doi: 10.1145/3603955.3603960.C.-Y. Huang, C.-L. Yang, and Y.-H. Hsiao, “A novel framework for mining social media data based on text mining, topic modeling, random forest, and danp methods,” Mathematics, vol. 9, no. 17, 2021, doi: 10.3390/math9172041.A. Jabbar, P. Akhtar, and S. Dani, “Real-time big data processing for instantaneous marketing decisions: A problematization approach,” Industrial Marketing Management, vol. 90, pp. 558–569, 2020, doi: 10.1016/j.indmarman.2019.09.001.R. Math, Big data analytics: Recent and emerging application in services industry, vol. 654. 2018. doi: 10.1007/978-981-10-6620-7_21.H. Zhang, Z. Zang, H. Zhu, M. I. Uddin, and M. A. Amin, “Big data-assisted social media analytics for business model for business decision making system competitive analysis,” Inf Process Manag, vol. 59, no. 1, 2022, doi: 10.1016/j.ipm.2021.102762.G. A. de Oliveira, R. O. Albuquerque, C. A. B. de Andrade, R. T. de Sousa, A. L. S. Orozco, and L. J. G. Villalba, “Anonymous real-time analytics monitoring solution for decision making supported by sentiment analysis,” Sensors (Switzerland), vol. 20, no. 16, pp. 1–29, 2020, doi: 10.3390/s20164557.S. A. Farimani, M. V. Jahan, and A. Milani Fard, “From Text Representation to Financial Market Prediction: A Literature Review,” Information (Switzerland), vol. 13, no. 10, 2022, doi: 10.3390/info13100466.P. Nanda and V. Kumar, “Information Processing and Data Analytics for Decision Making: A Journey from Traditional to Modern Approaches,” Information Resources Management Journal, vol. 35, no. 2, 2022, doi: 10.4018/IRMJ.291693.R. A. Camama, J. M. Baldelomar, J. M. Claveria, M. C. Espinas, J. M. Cabardo, and L. W. Rabago, “Q-DAR: Quick disaster aid and response model using Naïve Bayes and Bag-of-Words algorithm,” in IOP Conference Series: Materials Science and Engineering, 2019. doi: 10.1088/1757-899X/482/1/012051.A. S. Yüksel and F. G. Tan, “A real-time social network-based knowledge discovery system for decision making,” Automatika, vol. 59, no. 3, pp. 262–274, 2018, doi: 10.1080/00051144.2018.1531214.M. Arafeh, P. Ceravolo, A. Mourad, E. Damiani, and E. Bellini, “Ontology based recommender system using social network data,” Future Generation Computer Systems, vol. 115, pp. 769–779, 2021, doi: 10.1016/j.future.2020.09.030.S. Ahmadi, S. Shokouhyar, M. Amerioun, and N. Salehi Tabrizi, “A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks,” Journal of Retailing and Consumer Services, vol. 76, 2024, doi: 10.1016/j.jretconser.2023.103540.S. Ahmadi, S. Shokouhyar, M. H. Shahidzadeh, and I. 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