Análisis de sentimiento en las redes sociales: un análisis bibliométrico completo

Contenido principal del artículo

Tanase Tasente
Maria-Alina Caratas

Resumen

Esta investigación tiene como objetivo presentar una revisión bibliométrica del análisis de sentimientos en las redes sociales, destacando su evolución, contribuyentes clave y temas emergentes. El estudio utiliza la base de datos Clarivate Web of Science y emplea una metodología de búsqueda estratégica para garantizar resultados precisos. Con la ayuda de RStudio y el paquete “bibliometrix”, la investigación emplea un enfoque riguroso para analizar 764 artículos, enfatizando temas, patrones de citación y dinámicas colaborativas. Los resultados de la investigación de 2011 a 2023 revelaron un crecimiento significativo en el ámbito del análisis de sentimiento, con una tasa de crecimiento anual de casi el 40%. La naturaleza colaborativa de este ámbito es evidente: las colaboraciones internacionales constituyen el 27%. Geográficamente, dominan Estados Unidos, China e India, aunque hay aportaciones significativas de varios países de todo el mundo. El estudio enfatiza el atractivo universal del análisis de sentimientos y se muestra prometedor para aplicaciones y metodologías innovadoras. Esta revisión bibliométrica es una exploración integral del análisis de sentimientos en las redes sociales, presentando ideas sobre la progresión del campo, la naturaleza interdisciplinaria y las posibles trayectorias futuras. Subraya la importancia de las colaboraciones internacionales y la investigación impulsada por la calidad, sugiriendo vías para la exploración futura en el panorama dinámico de la comunicación digital.

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Tasente, T., & Caratas, M.-A. (2024). Análisis de sentimiento en las redes sociales: un análisis bibliométrico completo . AdComunica, (28), 243–270. https://doi.org/10.6035/adcomunica.7819
Número
Sección
Otras investigaciones
Biografía del autor/a

Tanase Tasente, Ovidius University of Constanta, Romania - Faculty of Law and Administrative Sciences

Tanase Tasente [tanase.tasente@365.univ-ovidius.ro] is lecturer and ERASMUS coordinator at the Faculty of Law and Administrative Sciences at Ovidius University in Constanța. He holds a bachelor’s degree, a master’s degree, and a Ph.D. in Communication Sciences, and another master’s degree in European Administration, Institutions, and Public Policies. He has written over 100 scientific papers and six books, focusing on the use of social media by institutions and on public policy strategies.

Maria-Alina Caratas, Ovidius University of Constanta, Romania - Faculty of Law and Administrative Sciences

Maria-Alina Caratas [maria.caratas@365.univ-ovidius.ro] is affiliated at the Faculty of Law and Administrative Sciences at Ovidius University of Constanta. Her educational background is multidisciplinary, holding a degree in Economic Sciences with a specialization in International Economic Relations, a master’s in Management and Administration of SMEs, and a PhD in accounting. Her doctoral thesis focused on internal audit, internal control, and organizational culture. Her research interests spans CSR, corporate governance, organizational culture, business and sustainability.

Citas

Aria, M., & Cuccurullo, C. (2023). bibliometrix: Comprehensive Science Mapping Analysis (4.1.3). Retrieved 11 July 2024 at https://cran.r-project.org/web/packages/bibliometrix/index.html

Baek, T. H., & Yi, K. (2024). A computational approach to cryptocurrency marketing on social media. In: International Journal of Advertising. DOI: https://doi.org/10.1080/02650487.2024.2362472

Carvache-Franco, O., Carvache-Franco, M., Carvache-Franco, W., & Iturralde, K. (2023). Topic and sentiment analysis of crisis communications about the COVID-19 pandemic in Twitter’s tourism hashtags. In: Tourism and Hospitality Research, Vol. 23, nº1, 44–59. DOI: https://doi.org/10.1177/14673584221085470

Chen, Z., & Kwak, D. H. (2023). It’s Okay to be Not Okay: An Analysis of Twitter Responses to Naomi Osaka’s Withdrawal due to Mental Health Concerns. In: Communication & Sport, Vol. 11, n3, pp. 439–461. DOI: https://doi.org/10.1177/21674795221141328

Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach fordetecting, quantifying, and visualizing the evolution of a research field: A practical application tothe fuzzy sets theory field. In: Journal of Informetrics, Vol. 5, n1, 146-166.

Cranmer, G. A., Peltz, S., Boatwright, B., Sanderson, J., & Scheinbaum, A. (2023). Athletes’ displaced dissent on social media: Triggering agents, message strategies, and user-generated responses. In: Communication Quarterly, Vol. 71, n4, 343–366. DOI: https://doi.org/10.1080/01463373.2023.2203828

Del Valle Martin, E., & de la Fuente Valentin, L. (2023). Sentiment analysis methods for politics and hate speech contents in Spanish language: A systematic review. In: IEEE Latin America Transactions, Vol. 21, n3, 408–418. DOI: https://doi.org/10.1109/TLA.2023.10068844

Demczuk, R., Manosso, F. C., da Silva, J. L., & Schiessl, D. (2022). Sentiment analysis and effective social media communication: A low-income country case during a Covid-19 pandemic. In: Revista Brasileira de Marketing, Vol. 21, n3, 942–1004. DOI: https://doi.org/10.5585/remark.v21i3.19271

Deori, M., Nisha, F., Verma, N. K., & Verma, M. K. (2023). Consumption Patterns of Female Lifestyle Influencers During Covid-19 Pandemic: A Thematic Sentiment Analysis Based on the Comments of Selected YouTube Videos. In: Journal of Creative Communications. DOI: https://doi.org/10.1177/09732586231168489

Fahmi, B. M. (2024). Falsehood on social media in Egypt: Rumour detection and sentiment analysis of users’ comments. In: Journal of Arab & Muslim Media Research, Vol. 17, N1, 129–161. DOI: https://doi.org/10.1386/jammr_00069_1

Fleet, R. W., & Hine, K. A. (2022). Surprise, anticipation, sadness, and fear: A sentiment analysis of social media’s portrayal of police use of facial recognition technology. In: Policing-A Journal of Policy and Practice, Vol. 16, n4, 630–647. DOI: https://doi.org/10.1093/police/paab083

Goldman, R. T., Mcbride, S. K., Stovall, W. K., & Damby, D. E. (2024). USGS and social media user dialogue and sentiment during the 2018 eruption of Kīlauea Volcano, Hawai’i. En: Frontiers in Communication, Vol. 9. DOI: https://doi.org/10.3389/fcomm.2024.986974

Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2023). More than a feeling: Accuracy and application of sentiment analysis. In: International Journal of Research in Marketing, Vol.40, n1, 75–87. DOI: https://doi.org/10.1016/j.ijresmar.2022.05.005

Jindal, K., & Aron, R. (2021). A novel visual-textual sentiment analysis framework for social media data. In: Cognitive computation, Vol.13, n6, 1433–1450. DOI: https://doi.org/10.1007/s12559-021-09929-3

Kaur, M., Verma, R., & Otoo, F. N. K. (2021a). Emotions in leader’s crisis communication: Twitter sentiment analysis during Covid-19 outbreak. In: Journal of human behavior in The social environment, Vol. 31, n1–4, 362–372. DOI: https://doi.org/10.1080/10911359.2020.1829239

Kaur, M., Verma, R., & Ranjan, S. (2021b). Political leaders’ communication: A twitter sentiment analysis during Covid-19 pandemic. In: Jurnal The Messenger, Vol. 13, n1, 45–62. DOI: https://doi.org/10.26623/themessenger.v13i1.2585

Mann, S., Arora, J., Bhatia, M., Sharma, R., & Taragi, R. (2023). Twitter sentiment analysis using enhanced BERT. In: A. Kulkarni, S. Mirjalili, & S. Udgata (Eds.), Intelligent Systems and Applications, ICISA 2022, Vol.959, 263–271. DOI: https://doi.org/10.1007/978-981-19-6581-4_21

Mi, Z., & Zhan, H. (2023). Text mining attitudes toward climate change: Emotion and sentiment analysis of the Twitter corpus. In: Weather Climate and Society, Vol. 15, n2, 277–287. DOI: https://doi.org/10.1175/WCAS-D-22-0123.1

Minango Negrete, J. C., Iano, Y., Minango Negrete, P. D., Vaz, G. C., & de Oliveira, G. G. (2023). Sentiment analysis in the ecuadorian presidential election. In: Y. Iano, O. Saotome, G. Vasquez, C. Pezzuto, R. Arthur, & G. DeOliveira (Eds.), Proceedings of the 7th Brazilian technology symposium (BTSYM 21): Emerging trends in human smart and sustainable future of cities, Vol. 207, 25–34. DOI: https://doi.org/10.1007/978-3-031-04435-9_3

Ngou Njikam Abdou, A. L., & Fute Tagne, E. (2021). A sentiment analysis approach for abusive content detection using improved dataset. In: 2021 International conference on computational science and computational intelligence (CSCI 2021), 1415–1420. DOI: https://doi.org/10.1109/CSCI54926.2021.00283

Park, S., Strover, S., Choi, J., & Schnell, M. (2023). Mind games: A temporal sentiment analysis of the political messages of the Internet Research Agency on Facebook and Twitter. In: New Media & Society, Vol. 25, n3, 463–484. DOI: https://doi.org/10.1177/14614448211014355

Peterson, J., Densley, J., Spaulding, J., & Higgins, S. (2023). How Mass Public Shooters Use Social Media: Exploring Themes and Future Directions. In: Social Media + Society, Vol.9, n1. DOI: https://doi.org/10.1177/20563051231155101

Pradhan, R., & Sharma, D. K. (2023). An ensemble deep learning classifier for sentiment analysis on code-mix Hindi-English data. In: Soft Computing, Vol. 27, n15, 11053. DOI: https://doi.org/10.1007/s00500-022-07091-y

Sabol, R., & Horak, A. (2022). New language identification and sentiment analysis modules for social media communication. In: P. Sojka, A. Horak, I. Kopecek, & K. Pala (Eds.), Text, Speech, and Dialogue (TSD 2022), Vol.13502, 89–101. DOI: https://doi.org/10.1007/978-3-031-16270-1_8

Trivedi, S. K., & Singh, A. (2021). Twitter sentiment analysis of app based online food delivery companies. In: Global Knowledge Memory and Communication, Vol.70, n8–9, 891–910. DOI: https://doi.org/10.1108/GKMC-04-2020-0056

Xie, T., Ge, Y., Xu, Q., & Chen, S. (2023). Public awareness and sentiment analysis of Covid-Related discussions using BERT-Based infoveillance. In: Acta Iranica: Encyclopédie permanente des études iraniennes, Vol. 4, n1, 333–347. DOI: https://doi.org/10.3390/ai4010016

Xu, J., Guo, D., Zhao, Z., & Liu, S. (2024). How social media expedites the crisis spillover effect: A case study of Tesla’s recall event. In: Public Relations Review, Vol. 50, n1. DOI: https://doi.org/10.1016/j.pubrev.2024.102432

Yaqub, U., Chun, S. A., Atluri, V., & Vaidya, J. (2021). Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling. In: Information Polity, Vol. 26, n4, 375–390. DOI: https://doi.org/10.3233/IP-210321

Yin, J. Y. B., Saad, N. H. M., & Yaacob, Z. (2022). Exploring sentiment analysis on E-commerce business: Lazada and shopee. In: TEM Journal-Technology Education Management Informatics, Vol. 11, n4, 1508–1519. DOI: https://doi.org/10.18421/TEM114-11