Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture

The COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. Thi...

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Autores:
Henríquez Miranda, Carlos
Sanchez Torres, German
Salcedo, Dixon
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10569
Acceso en línea:
https://hdl.handle.net/11323/10569
https://repositorio.cuc.edu.co
Palabra clave:
Deep learning
Fine-tuning
Natural language processing
Evolution of feelings
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openAccess
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Atribución 4.0 Internacional (CC BY 4.0)
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/10569
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repository_id_str
dc.title.eng.fl_str_mv Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
title Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
spellingShingle Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
Deep learning
Fine-tuning
Natural language processing
Evolution of feelings
title_short Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
title_full Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
title_fullStr Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
title_full_unstemmed Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
title_sort Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architecture
dc.creator.fl_str_mv Henríquez Miranda, Carlos
Sanchez Torres, German
Salcedo, Dixon
dc.contributor.author.none.fl_str_mv Henríquez Miranda, Carlos
Sanchez Torres, German
Salcedo, Dixon
dc.subject.proposal.eng.fl_str_mv Deep learning
Fine-tuning
Natural language processing
Evolution of feelings
topic Deep learning
Fine-tuning
Natural language processing
Evolution of feelings
description The COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. This study explores the evolution of sentiment in Spanish pandemic tweets through a data analysis based on a fine-tuned BERT architecture. A total of six million tweets were collected using web scraping techniques, and pre-processing was applied to filter and clean the data. The fine-tuned BERT architecture was utilized to perform sentiment analysis, which allowed for a deeplearning approach to sentiment classification. The analysis results were graphically represented based on search criteria, such as “COVID-19” and “coronavirus”. This study reveals sentiment trends, significant concerns, relationship with announced news, public reactions, and information dissemination, among other aspects. These findings provide insight into the emotional impact of the COVID-19 pandemic on individuals and the corresponding impact on social media platforms.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-10-31T17:04:10Z
dc.date.available.none.fl_str_mv 2023-10-31T17:04:10Z
dc.date.issued.none.fl_str_mv 2023-05-29
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Miranda, C.H.; Sanchez-Torres, G.; Salcedo, D. Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture. Data 2023, 8, 96. https://doi.org/10.3390/data8060096
dc.identifier.issn.spa.fl_str_mv 2306-5729
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10569
dc.identifier.doi.none.fl_str_mv 10.3390/data8060096
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co
identifier_str_mv Miranda, C.H.; Sanchez-Torres, G.; Salcedo, D. Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture. Data 2023, 8, 96. https://doi.org/10.3390/data8060096
2306-5729
10.3390/data8060096
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10569
https://repositorio.cuc.edu.co
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language eng
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dc.relation.references.spa.fl_str_mv 1. Smith, B.; Lim, M. How the COVID-19 Pandemic Is Focusing Attention on Loneliness and Social Isolation. Public Health Res. Pract. 2020, 30, 3022008. [CrossRef] [PubMed]
2. Hwang, T.-J.; Rabheru, K.; Peisah, C.; Reichman, W.; Ikeda, M. Loneliness and Social Isolation during the COVID-19 Pandemic. Int. Psychogeriatr. 2020, 32, 1217–1220. [CrossRef]
3. Pokharel, B.P. Twitter Sentiment Analysis During Covid-19 Outbreak in Nepal 2020. SSRN 2020. [CrossRef]
4. Henríquez, C.; Guzmán, J.; Salcedo, D. Minería de Opiniones Basado En La Adaptación al Español de ANEW Sobre Opiniones Acerca de Hoteles Opinion. Proces. Leng. Nat. 2016, 41, 25–32.
5. Henríquez Miranda, C.; Guzmán, J. Information Extraction from the Web to Identify Actions of an Automated Planning Domain Mode. Ingeniare J. 2015, 23, 439–448.
6. Pak, A.; Paroubek, P. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, 19–21 May 2010; pp. 1320–1326.
7. González-Padilla, D.A.; Tortolero-Blanco, L. Social Media Influence in the COVID-19 Pandemic. Int. Braz. J. Urol. 2020, 46, 120–124. [CrossRef]
8. Henríquez, C.; Guzmán, J. A Review of Sentiment Analysis in Spanish. Tecciencia 2017, 12, 35–48. [CrossRef]
9. Hurtado, L.; Pla, F. Análisis de Sentimientos, Detección de Tópicos y Análisis de Sentimientos de Aspectos En Twitter. In Proceedings of the TASS 2014, Girona, Spain, 16–19 September 2014.
10. Hung, C.; Chen, S.-J. Word Sense Disambiguation Based Sentiment Lexicons for Sentiment Classification. Knowl.-Based Syst. 2016, 110, 224–232. [CrossRef]
11. Henriquez Miranda, C.; Sanchez, G. Aspect Extraction for Opinion Mining with a Semantic Model. Eng. Lett. 2021, 29, 61–67.
12. Henríquez, C.; Plà, F.; Hurtado, L.F.; Luna, J.A.G. Análisis de Sentimientos a Nivel de Aspecto Usando Ontologías y Aprendizaje Automático. Proces. Leng. Nat. 2017, 59, 49–56.
13. Zhang, L.; Wang, S.; Liu, B. Deep Learning for Sentiment Analysis: A Survey. WIREs Data Min. Knowl. Discov. 2018, 8, e1253. [CrossRef]
14. Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv 2019, arXiv:1810.04805.
15. Jojoa, M.; Garcia-Zapirain, B.; Gonzalez, M.J.; Perez-Villa, B.; Urizar, E.; Ponce, S.; Tobar-Blandon, M.F. Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques. Int. J. Environ. Res. Public Health 2022, 19, 5705. [CrossRef]
16. Liu, B. Sentiment Analysis and Opinion Mining. Sentim. Anal. Opin. Min. 2012, 5, 1–167.
17. Vilares, D.; Alonso Perdo, M.Á.; Carlos, G.-R. A Syntactic Approach for Opinion Mining on Spanish Reviews. Nat. Lang. Eng. 2013, 1, 139–163.
18. Plaza-Del-Arco, F.M.; Martín-Valdivia, M.T.; María Jiménez-Zafra, S.; Molina-González, M.D.; Martínez-Cámara, E. COPOS: Corpus Of Patient Opinions in Spanish. Application of Sentiment Analysis Techniques. Proces. Leng. Nat. 2016, 57, 83–90.
19. Cadilhac, A.; Benamara, F.; Aussenac-Gilles, N. Ontolexical Resources for Feature Based Opinion Mining: A Case-Study. In Proceedings of the 6th Workshop on Ontologies and Lexical Resources, Beijing, China, 22 August 2010; pp. 77–86.
20. Steinberger, J.; Brychcín, T.; Konkol, M. Aspect-Level Sentiment Analysis in Czech. In Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, Maryland, 27 June 2014; pp. 24–30.
21. Pang, B.; Lee, L. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2008, 2, 1–135. [CrossRef]
22. De Freitas, L.A.; Vieira, R. Ontology-Based Feature Level Opinion Mining for Portuguese Reviews. In Proceedings of the Proceedings of the 22nd International Conference on World Wide Web; ACM: New York, NY, USA, 2013; pp. 367–370.
23. Manek, A.S.; Shenoy, P.D.; Mohan, M.C. Aspect Term Extraction for Sentiment Analysis in Large Movie Reviews Using Gini Index Feature Selection Method and SVM Classifier. World Wide Web 2016, 20, 135–154. [CrossRef]
24. Agüero-Torales, M.M.; Abreu Salas, J.I.; López-Herrera, A.G. Deep Learning and Multilingual Sentiment Analysis on Social Media Data: An Overview. Appl. Soft Comput. 2021, 107, 107373. [CrossRef]
25. Kaur, H.; Ahsaan, S.U.; Alankar, B.; Chang, V. A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Inf. Syst. Front. 2021, 23, 1417–1429. [CrossRef]
26. Jing, N.; Wu, Z.; Wang, H. A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction. Expert Syst. Appl. 2021, 178, 115019. [CrossRef]
27. Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; van der Laak, J.A.W.M.; van Ginneken, B.; Sánchez, C.I. A Survey on Deep Learning in Medical Image Analysis. Med. Image Anal. 2017, 42, 60–88. [CrossRef] [PubMed]
28. Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Sciarretta, L.; Ursino, D.; Virgili, L. A Space-Time Framework for Sentiment Scope Analysis in Social Media. Big Data Cogn. Comput. 2022, 6, 130. [CrossRef]
29. Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L. New Approaches to Extract Information From Posts on COVID-19 Published on Reddit. Int. J. Info. Tech. Dec. Mak. 2022, 21, 1385–1431. [CrossRef]
30. Manguri, K.H.; Ramadhan, R.N.; Amin, P.R.M. Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks. Kurd. J. Appl. Res. 2020, 5, 54–65. [CrossRef]
31. Dubey, A.D. Twitter Sentiment Analysis during COVID-19 Outbreak. SSRN 2020. preprint. [CrossRef]
32. Boon-Itt, S.; Skunkan, Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health Surveill. 2020, 6, e21978. [CrossRef]
33. Garcia, K.; Berton, L. Topic Detection and Sentiment Analysis in Twitter Content Related to COVID-19 from Brazil and the USA. Appl. Soft Comput. 2021, 101, 107057. [CrossRef]
34. Kruspe, A.; Häberle, M.; Kuhn, I.; Zhu, X. Cross-Language Sentiment Analysis of European Twitter Messages during the COVID-19 Pandemic. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online, 9–10 July 2020.
35. Marcec, R.; Likic, R. Using Twitter for Sentiment Analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 Vaccines. Postgrad. Med. J. 2022, 98, 544–550. [CrossRef]
36. Villavicencio, C.; Macrohon, J.J.; Inbaraj, X.A.; Jeng, J.-H.; Hsieh, J.-G. Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes. Information 2021, 12, 204. [CrossRef]
37. Valle-Cruz, D.; Fernandez, V.; Lopez-Chau, A.; Sandoval Almazan, R. Does Twitter Affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19. Cogn. Comput. 2020. preprint. [CrossRef]
38. Sanders, A.; White, R.; Severson, L.S.; Ma, R.; McQueen, R.; Paulo, H.C.A.; Zhang, Y.; Erickson, J.S.; Bennett, K.P. Unmasking the Conversation on Masks: Natural Language Processing for Topical Sentiment Analysis of COVID-19 Twitter Discourse. medRxiv 2020. [CrossRef]
39. Khan, R.; Shrivastava, P.; Kapoor, A.; Tiwari, A.; Mittal, A. Social Media Analysis with AI: Sentiment Analysis Techniques for the Analysis of Twitter COVID-19 Data. J. Crit. Rev. 2020, 7, 2020.
40. Rodríguez-Orejuela, A.; Montes-Mora, C.L.; Osorio-Andrade, C.F. Sentimientos hacia la vacunación contra la COVID-19: Panorama colombiano en Twitter. Palabra Clave 2022, 25, e2514. [CrossRef]
41. Aislamiento Social Obligatorio: Un Análisis de Sentimientos Mediante Machine Learning. Available online: http://www.scielo. org.co/scielo.php?script=sci_arttext&pid=S2215-910X2021000100001 (accessed on 4 August 2022).
42. Ahmad, W.; Wang, B.; Martin, P.; Xu, M.; Xu, H. Enhanced Sentiment Analysis Regarding COVID-19 News from Global Channels. J. Comput. Soc. Sci. 2023, 6, 19–57. [CrossRef]
43. Kumari, S.; Pushphavathi, T.P. Intelligent Lead-Based Bidirectional Long Short Term Memory for COVID-19 Sentiment Analysis. Soc. Netw. Anal. Min. 2022, 13, 1. [CrossRef] [PubMed]
44. Cañete, J.; Chaperon, G.; Fuentes, R.; Ho, J.-H.; Kang, H.; Pérez, J. Spanish Pre-Trained BERT Model and Evaluation Data. In Proceedings of the PML4DC at ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020.
45. de Arriba Serra, A.; Oriol Hilari, M.; Franch Gutiérrez, J. Applying Sentiment Analysis on Spanish Tweets Using BETO. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021): Co-located with the Conference of the Spanish Society for Natural Language Processing (SEPLN 2021), XXXVII International Conference of the Spanish Society for Natural Language Processing, Málaga, Spain, 24 September 2021; pp. 1–8.
46. Pijal, W.; Armijos, A.; Llumiquinga, J.; Lalvay, S.; Allauca, S.; Cuenca, E. Spanish Pre-Trained CaTrBETO Model for Sentiment Classification in Twitter. In Proceedings of the 2022 Third International Conference on Information Systems and Software Technologies (ICI2ST), Quito, Ecuador, 8–10 November 2022; pp. 93–98.
47. Vernikou, S.; Lyras, A.; Kanavos, A. Multiclass Sentiment Analysis on COVID-19-Related Tweets Using Deep Learning Models. Neural. Comput. Appl. 2022, 34, 19615–19627. [CrossRef]
48. Jojoa, M.; Eftekhar, P.; Nowrouzi-Kia, B.; Garcia-Zapirain, B. Natural Language Processing Analysis Applied to COVID-19 Open-Text Opinions Using a DistilBERT Model for Sentiment Categorization. AI Soc. 2022, 2022, 1–8. [CrossRef]
49. Madani, Y.; Erritali, M.; Bouikhalene, B. A New Sentiment Analysis Method to Detect and Analyse Sentiments of COVID-19 Moroccan Tweets Using a Recommender Approach. Multimed. Tools Appl. 2023, 1–20. [CrossRef]
50. Natural Language Processing: State of the Art, Current Trends and Challenges | SpringerLink. Available online: https://link. springer.com/article/10.1007/s11042-022-13428-4 (accessed on 21 February 2023).
51. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; Polosukhin, I. Attention Is All You Need. arXiv 2017, arXiv:1706.03762.
52. Shi, Y.; Wang, J.; Ren, P.; ValizadehAslani, T.; Zhang, Y.; Hu, M.; Liang, H. Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment. J. Biomed. Inform. 2023, 138, 104285. [CrossRef] [PubMed]
53. Kong, J.; Wang, J.; Zhang, X. Hierarchical BERT with an Adaptive Fine-Tuning Strategy for Document Classification. Knowl.-Based Syst. 2022, 238, 107872. [CrossRef]
54. Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; Liu, P.J. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 2020, 21, 5485–5551.
55. Bert-Base-Uncased. Hugging Face. Available online: https://huggingface.co/bert-base-uncased (accessed on 2 December 2022).
56. Pérez, J.M.; Furman, D.A.; Alonso Alemany, L.; Luque, F.M. RoBERTuito: A Pre-Trained Language Model for Social Media Text in Spanish. In Proceedings of the Thirteenth Language Resources and Evaluation Conference; European Language Resources Association, Marseille, France, 20–25 June 2022; pp. 7235–7243.
57. Felbo, B.; Mislove, A.; Søgaard, A.; Rahwan, I.; Lehmann, S. Using Millions of Emoji Occurrences to Learn Any-Domain Representations for Detecting Sentiment, Emotion and Sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics, Copenhagen, Denmark, 9–11 September 2017; pp. 1615–1625.
58. Novak, P.K.; Smailovi´c, J.; Sluban, B.; Mozetiˇc, I. Sentiment of Emojis. PLoS ONE 2015, 10, e0144296. [CrossRef]
59. Amrullah, M.S.; Budi, I.; Santoso, A.B.; Putra, P.K. The Effect of Using Emoji and Hashtag in Sentiment Analysis on Twitter Case Study: Indonesian Online Travel Agent. AIP Conf. Proc. 2023, 2654, 020013. [CrossRef]
60. Ayvaz, S.; Shiha, M.O. The Effects of Emoji in Sentiment Analysis. Int. J. Comput. Electr. Eng. 2017, 9, 360–369. [CrossRef]
61. Kejriwal, M.; Wang, Q.; Li, H.; Wang, L. An Empirical Study of Emoji Usage on Twitter in Linguistic and National Contexts. Online Soc. Netw. Media 2021, 24, 100149. [CrossRef]
62. Miller, H.; Thebault-Spieker, J.; Chang, S.; Johnson, I.; Terveen, L.; Hecht, B. “blissfully Happy” or “Ready to Fight”: Varying Interpretations of Emoji. In Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016, Cologne, Germany, 17–20 May 2016; AAAI Press: Washington, DC, USA, 2016; pp. 259–268.
63. Barbieri, F.; Espinosa-Anke, L.; Saggion, H. Revealing Patterns of Twitter Emoji Usage in Barcelona and Madrid. Artif. Intell. Res. Dev. 2016, 288, 239–244. [CrossRef]
64. Wijeratne, S.; Balasuriya, L.; Sheth, A.; Doran, D. EmojiNet: Building a Machine Readable Sense Inventory for Emoji. In Proceedings of the Social Informatics, Bellevue, WA, USA, 11–14 November 2016; Spiro, E., Ahn, Y.-Y., Eds.; Springer International Publishing: Cham, Swizterland, 2016; pp. 527–541.
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spelling Atribución 4.0 Internacional (CC BY 4.0)Copyright: © 2023 by the authors.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Henríquez Miranda, CarlosSanchez Torres, GermanSalcedo, Dixon2023-10-31T17:04:10Z2023-10-31T17:04:10Z2023-05-29Miranda, C.H.; Sanchez-Torres, G.; Salcedo, D. Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture. Data 2023, 8, 96. https://doi.org/10.3390/data80600962306-5729https://hdl.handle.net/11323/1056910.3390/data8060096Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.coThe COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. This study explores the evolution of sentiment in Spanish pandemic tweets through a data analysis based on a fine-tuned BERT architecture. A total of six million tweets were collected using web scraping techniques, and pre-processing was applied to filter and clean the data. The fine-tuned BERT architecture was utilized to perform sentiment analysis, which allowed for a deeplearning approach to sentiment classification. The analysis results were graphically represented based on search criteria, such as “COVID-19” and “coronavirus”. This study reveals sentiment trends, significant concerns, relationship with announced news, public reactions, and information dissemination, among other aspects. These findings provide insight into the emotional impact of the COVID-19 pandemic on individuals and the corresponding impact on social media platforms.18 páginasapplication/pdfengMDPI AGSuizahttps://www.mdpi.com/2306-5729/8/6/96Exploring the evolution of sentiment in spanish pandemic tweets: a data analysis based on a fine-tuned bert architectureArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Data1. Smith, B.; Lim, M. How the COVID-19 Pandemic Is Focusing Attention on Loneliness and Social Isolation. Public Health Res. Pract. 2020, 30, 3022008. [CrossRef] [PubMed]2. Hwang, T.-J.; Rabheru, K.; Peisah, C.; Reichman, W.; Ikeda, M. Loneliness and Social Isolation during the COVID-19 Pandemic. Int. Psychogeriatr. 2020, 32, 1217–1220. [CrossRef]3. Pokharel, B.P. Twitter Sentiment Analysis During Covid-19 Outbreak in Nepal 2020. SSRN 2020. [CrossRef]4. Henríquez, C.; Guzmán, J.; Salcedo, D. Minería de Opiniones Basado En La Adaptación al Español de ANEW Sobre Opiniones Acerca de Hoteles Opinion. Proces. Leng. Nat. 2016, 41, 25–32.5. Henríquez Miranda, C.; Guzmán, J. Information Extraction from the Web to Identify Actions of an Automated Planning Domain Mode. Ingeniare J. 2015, 23, 439–448.6. Pak, A.; Paroubek, P. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, 19–21 May 2010; pp. 1320–1326.7. González-Padilla, D.A.; Tortolero-Blanco, L. Social Media Influence in the COVID-19 Pandemic. Int. Braz. J. Urol. 2020, 46, 120–124. [CrossRef]8. Henríquez, C.; Guzmán, J. A Review of Sentiment Analysis in Spanish. Tecciencia 2017, 12, 35–48. [CrossRef]9. Hurtado, L.; Pla, F. Análisis de Sentimientos, Detección de Tópicos y Análisis de Sentimientos de Aspectos En Twitter. In Proceedings of the TASS 2014, Girona, Spain, 16–19 September 2014.10. Hung, C.; Chen, S.-J. Word Sense Disambiguation Based Sentiment Lexicons for Sentiment Classification. Knowl.-Based Syst. 2016, 110, 224–232. [CrossRef]11. Henriquez Miranda, C.; Sanchez, G. Aspect Extraction for Opinion Mining with a Semantic Model. Eng. Lett. 2021, 29, 61–67.12. Henríquez, C.; Plà, F.; Hurtado, L.F.; Luna, J.A.G. Análisis de Sentimientos a Nivel de Aspecto Usando Ontologías y Aprendizaje Automático. Proces. Leng. Nat. 2017, 59, 49–56.13. Zhang, L.; Wang, S.; Liu, B. Deep Learning for Sentiment Analysis: A Survey. WIREs Data Min. Knowl. Discov. 2018, 8, e1253. [CrossRef]14. Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv 2019, arXiv:1810.04805.15. Jojoa, M.; Garcia-Zapirain, B.; Gonzalez, M.J.; Perez-Villa, B.; Urizar, E.; Ponce, S.; Tobar-Blandon, M.F. Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques. Int. J. Environ. Res. Public Health 2022, 19, 5705. [CrossRef]16. Liu, B. Sentiment Analysis and Opinion Mining. Sentim. Anal. Opin. Min. 2012, 5, 1–167.17. Vilares, D.; Alonso Perdo, M.Á.; Carlos, G.-R. A Syntactic Approach for Opinion Mining on Spanish Reviews. Nat. Lang. Eng. 2013, 1, 139–163.18. Plaza-Del-Arco, F.M.; Martín-Valdivia, M.T.; María Jiménez-Zafra, S.; Molina-González, M.D.; Martínez-Cámara, E. COPOS: Corpus Of Patient Opinions in Spanish. Application of Sentiment Analysis Techniques. Proces. Leng. Nat. 2016, 57, 83–90.19. Cadilhac, A.; Benamara, F.; Aussenac-Gilles, N. Ontolexical Resources for Feature Based Opinion Mining: A Case-Study. In Proceedings of the 6th Workshop on Ontologies and Lexical Resources, Beijing, China, 22 August 2010; pp. 77–86.20. Steinberger, J.; Brychcín, T.; Konkol, M. Aspect-Level Sentiment Analysis in Czech. In Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, Maryland, 27 June 2014; pp. 24–30.21. Pang, B.; Lee, L. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2008, 2, 1–135. [CrossRef]22. De Freitas, L.A.; Vieira, R. Ontology-Based Feature Level Opinion Mining for Portuguese Reviews. In Proceedings of the Proceedings of the 22nd International Conference on World Wide Web; ACM: New York, NY, USA, 2013; pp. 367–370.23. Manek, A.S.; Shenoy, P.D.; Mohan, M.C. Aspect Term Extraction for Sentiment Analysis in Large Movie Reviews Using Gini Index Feature Selection Method and SVM Classifier. World Wide Web 2016, 20, 135–154. [CrossRef]24. Agüero-Torales, M.M.; Abreu Salas, J.I.; López-Herrera, A.G. Deep Learning and Multilingual Sentiment Analysis on Social Media Data: An Overview. Appl. Soft Comput. 2021, 107, 107373. [CrossRef]25. Kaur, H.; Ahsaan, S.U.; Alankar, B.; Chang, V. A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Inf. Syst. Front. 2021, 23, 1417–1429. [CrossRef]26. Jing, N.; Wu, Z.; Wang, H. A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction. Expert Syst. Appl. 2021, 178, 115019. [CrossRef]27. Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; van der Laak, J.A.W.M.; van Ginneken, B.; Sánchez, C.I. A Survey on Deep Learning in Medical Image Analysis. Med. Image Anal. 2017, 42, 60–88. [CrossRef] [PubMed]28. Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Sciarretta, L.; Ursino, D.; Virgili, L. A Space-Time Framework for Sentiment Scope Analysis in Social Media. Big Data Cogn. Comput. 2022, 6, 130. [CrossRef]29. Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L. New Approaches to Extract Information From Posts on COVID-19 Published on Reddit. Int. J. Info. Tech. Dec. Mak. 2022, 21, 1385–1431. [CrossRef]30. Manguri, K.H.; Ramadhan, R.N.; Amin, P.R.M. Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks. Kurd. J. Appl. Res. 2020, 5, 54–65. [CrossRef]31. Dubey, A.D. Twitter Sentiment Analysis during COVID-19 Outbreak. SSRN 2020. preprint. [CrossRef]32. Boon-Itt, S.; Skunkan, Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health Surveill. 2020, 6, e21978. [CrossRef]33. Garcia, K.; Berton, L. Topic Detection and Sentiment Analysis in Twitter Content Related to COVID-19 from Brazil and the USA. Appl. Soft Comput. 2021, 101, 107057. [CrossRef]34. Kruspe, A.; Häberle, M.; Kuhn, I.; Zhu, X. Cross-Language Sentiment Analysis of European Twitter Messages during the COVID-19 Pandemic. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online, 9–10 July 2020.35. Marcec, R.; Likic, R. Using Twitter for Sentiment Analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 Vaccines. Postgrad. Med. J. 2022, 98, 544–550. [CrossRef]36. Villavicencio, C.; Macrohon, J.J.; Inbaraj, X.A.; Jeng, J.-H.; Hsieh, J.-G. Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes. Information 2021, 12, 204. [CrossRef]37. Valle-Cruz, D.; Fernandez, V.; Lopez-Chau, A.; Sandoval Almazan, R. Does Twitter Affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19. Cogn. Comput. 2020. preprint. [CrossRef]38. Sanders, A.; White, R.; Severson, L.S.; Ma, R.; McQueen, R.; Paulo, H.C.A.; Zhang, Y.; Erickson, J.S.; Bennett, K.P. Unmasking the Conversation on Masks: Natural Language Processing for Topical Sentiment Analysis of COVID-19 Twitter Discourse. medRxiv 2020. [CrossRef]39. Khan, R.; Shrivastava, P.; Kapoor, A.; Tiwari, A.; Mittal, A. Social Media Analysis with AI: Sentiment Analysis Techniques for the Analysis of Twitter COVID-19 Data. J. Crit. Rev. 2020, 7, 2020.40. Rodríguez-Orejuela, A.; Montes-Mora, C.L.; Osorio-Andrade, C.F. Sentimientos hacia la vacunación contra la COVID-19: Panorama colombiano en Twitter. Palabra Clave 2022, 25, e2514. [CrossRef]41. Aislamiento Social Obligatorio: Un Análisis de Sentimientos Mediante Machine Learning. Available online: http://www.scielo. org.co/scielo.php?script=sci_arttext&pid=S2215-910X2021000100001 (accessed on 4 August 2022).42. Ahmad, W.; Wang, B.; Martin, P.; Xu, M.; Xu, H. Enhanced Sentiment Analysis Regarding COVID-19 News from Global Channels. J. Comput. Soc. Sci. 2023, 6, 19–57. [CrossRef]43. Kumari, S.; Pushphavathi, T.P. Intelligent Lead-Based Bidirectional Long Short Term Memory for COVID-19 Sentiment Analysis. Soc. Netw. Anal. Min. 2022, 13, 1. [CrossRef] [PubMed]44. Cañete, J.; Chaperon, G.; Fuentes, R.; Ho, J.-H.; Kang, H.; Pérez, J. Spanish Pre-Trained BERT Model and Evaluation Data. In Proceedings of the PML4DC at ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020.45. de Arriba Serra, A.; Oriol Hilari, M.; Franch Gutiérrez, J. Applying Sentiment Analysis on Spanish Tweets Using BETO. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021): Co-located with the Conference of the Spanish Society for Natural Language Processing (SEPLN 2021), XXXVII International Conference of the Spanish Society for Natural Language Processing, Málaga, Spain, 24 September 2021; pp. 1–8.46. Pijal, W.; Armijos, A.; Llumiquinga, J.; Lalvay, S.; Allauca, S.; Cuenca, E. Spanish Pre-Trained CaTrBETO Model for Sentiment Classification in Twitter. In Proceedings of the 2022 Third International Conference on Information Systems and Software Technologies (ICI2ST), Quito, Ecuador, 8–10 November 2022; pp. 93–98.47. Vernikou, S.; Lyras, A.; Kanavos, A. Multiclass Sentiment Analysis on COVID-19-Related Tweets Using Deep Learning Models. Neural. Comput. Appl. 2022, 34, 19615–19627. [CrossRef]48. Jojoa, M.; Eftekhar, P.; Nowrouzi-Kia, B.; Garcia-Zapirain, B. Natural Language Processing Analysis Applied to COVID-19 Open-Text Opinions Using a DistilBERT Model for Sentiment Categorization. AI Soc. 2022, 2022, 1–8. [CrossRef]49. Madani, Y.; Erritali, M.; Bouikhalene, B. A New Sentiment Analysis Method to Detect and Analyse Sentiments of COVID-19 Moroccan Tweets Using a Recommender Approach. Multimed. Tools Appl. 2023, 1–20. [CrossRef]50. Natural Language Processing: State of the Art, Current Trends and Challenges | SpringerLink. Available online: https://link. springer.com/article/10.1007/s11042-022-13428-4 (accessed on 21 February 2023).51. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; Polosukhin, I. Attention Is All You Need. arXiv 2017, arXiv:1706.03762.52. Shi, Y.; Wang, J.; Ren, P.; ValizadehAslani, T.; Zhang, Y.; Hu, M.; Liang, H. Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment. J. Biomed. Inform. 2023, 138, 104285. [CrossRef] [PubMed]53. Kong, J.; Wang, J.; Zhang, X. Hierarchical BERT with an Adaptive Fine-Tuning Strategy for Document Classification. Knowl.-Based Syst. 2022, 238, 107872. [CrossRef]54. Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; Liu, P.J. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 2020, 21, 5485–5551.55. Bert-Base-Uncased. Hugging Face. Available online: https://huggingface.co/bert-base-uncased (accessed on 2 December 2022).56. Pérez, J.M.; Furman, D.A.; Alonso Alemany, L.; Luque, F.M. RoBERTuito: A Pre-Trained Language Model for Social Media Text in Spanish. In Proceedings of the Thirteenth Language Resources and Evaluation Conference; European Language Resources Association, Marseille, France, 20–25 June 2022; pp. 7235–7243.57. Felbo, B.; Mislove, A.; Søgaard, A.; Rahwan, I.; Lehmann, S. Using Millions of Emoji Occurrences to Learn Any-Domain Representations for Detecting Sentiment, Emotion and Sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics, Copenhagen, Denmark, 9–11 September 2017; pp. 1615–1625.58. Novak, P.K.; Smailovi´c, J.; Sluban, B.; Mozetiˇc, I. Sentiment of Emojis. PLoS ONE 2015, 10, e0144296. [CrossRef]59. Amrullah, M.S.; Budi, I.; Santoso, A.B.; Putra, P.K. The Effect of Using Emoji and Hashtag in Sentiment Analysis on Twitter Case Study: Indonesian Online Travel Agent. AIP Conf. Proc. 2023, 2654, 020013. [CrossRef]60. Ayvaz, S.; Shiha, M.O. The Effects of Emoji in Sentiment Analysis. Int. J. Comput. Electr. Eng. 2017, 9, 360–369. [CrossRef]61. Kejriwal, M.; Wang, Q.; Li, H.; Wang, L. An Empirical Study of Emoji Usage on Twitter in Linguistic and National Contexts. Online Soc. Netw. Media 2021, 24, 100149. [CrossRef]62. Miller, H.; Thebault-Spieker, J.; Chang, S.; Johnson, I.; Terveen, L.; Hecht, B. “blissfully Happy” or “Ready to Fight”: Varying Interpretations of Emoji. In Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016, Cologne, Germany, 17–20 May 2016; AAAI Press: Washington, DC, USA, 2016; pp. 259–268.63. Barbieri, F.; Espinosa-Anke, L.; Saggion, H. Revealing Patterns of Twitter Emoji Usage in Barcelona and Madrid. Artif. Intell. Res. Dev. 2016, 288, 239–244. [CrossRef]64. Wijeratne, S.; Balasuriya, L.; Sheth, A.; Doran, D. EmojiNet: Building a Machine Readable Sense Inventory for Emoji. In Proceedings of the Social Informatics, Bellevue, WA, USA, 11–14 November 2016; Spiro, E., Ahn, Y.-Y., Eds.; Springer International Publishing: Cham, Swizterland, 2016; pp. 527–541.18168Deep learningFine-tuningNatural language processingEvolution of feelingsPublicationORIGINALExploring the Evolution of Sentiment in Spanish Pandemic Tweets A Data Analysis Based on a Fine-Tuned BERT Architecture.pdfExploring the Evolution of Sentiment in Spanish Pandemic Tweets A Data Analysis Based on a Fine-Tuned BERT Architecture.pdfArtículoapplication/pdf11038333https://repositorio.cuc.edu.co/bitstreams/f9ac9c11-c604-4d6f-a3e8-196948cf0a45/download33c76149674a8dd7e6d4b2f9fd087ee3MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/c8ea17af-0a20-41cf-8b5a-9af5f2bb846d/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTExploring the Evolution of Sentiment in Spanish Pandemic Tweets A Data Analysis Based on a Fine-Tuned BERT 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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