Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains

Knowing the state of public opinion regarding a product, the reputation of a celebrity or the assessment of a political candidate is very useful in today’s world. For example, in the particular case of a company, analyzing the opinions of its customers is very beneficial, since it allows to know the...

Full description

Autores:
Silva, Jesus
Vargas, Jesus
Cabrera, Danelys
García, Patricia
Bonerge Pineda, Omar
Marin Gonzalez, Fredy
Vargas Mercado, Carlos
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10754
Acceso en línea:
https://hdl.handle.net/11323/10754
https://repositorio.cuc.edu.co/
Palabra clave:
Polarity
Latent semantic analysis
Surface feature
Parse feature
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
title Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
spellingShingle Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
Polarity
Latent semantic analysis
Surface feature
Parse feature
title_short Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
title_full Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
title_fullStr Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
title_full_unstemmed Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
title_sort Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains
dc.creator.fl_str_mv Silva, Jesus
Vargas, Jesus
Cabrera, Danelys
García, Patricia
Bonerge Pineda, Omar
Marin Gonzalez, Fredy
Vargas Mercado, Carlos
dc.contributor.author.none.fl_str_mv Silva, Jesus
Vargas, Jesus
Cabrera, Danelys
García, Patricia
Bonerge Pineda, Omar
Marin Gonzalez, Fredy
Vargas Mercado, Carlos
dc.subject.proposal.eng.fl_str_mv Polarity
Latent semantic analysis
Surface feature
Parse feature
topic Polarity
Latent semantic analysis
Surface feature
Parse feature
description Knowing the state of public opinion regarding a product, the reputation of a celebrity or the assessment of a political candidate is very useful in today’s world. For example, in the particular case of a company, analyzing the opinions of its customers is very beneficial, since it allows to know the disconformities and, from these, to define new strategies that guarantee to obtain a greater degree of satisfaction in the users, as well as a greater success within the market. This article aims to develop a method for classifying the polarity of product aspects.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-04-09
dc.date.accessioned.none.fl_str_mv 2024-02-21T13:59:09Z
dc.date.available.none.fl_str_mv 2024-02-21T13:59:09Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Jesus Silva, Jesus Vargas, Danelys Cabrera, Patricia García, Omar Bonerge Pineda, Fredy Marin Gonzalez, Carlos Vargas Mercado, Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains, Procedia Computer Science,Volume 170, 2020,Pages 977-982, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.098.
dc.identifier.issn.spa.fl_str_mv 1877-0509
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10754
dc.identifier.doi.none.fl_str_mv 10.1016/j.procs.2020.03.098
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 Jesus Silva, Jesus Vargas, Danelys Cabrera, Patricia García, Omar Bonerge Pineda, Fredy Marin Gonzalez, Carlos Vargas Mercado, Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains, Procedia Computer Science,Volume 170, 2020,Pages 977-982, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.098.
1877-0509
10.1016/j.procs.2020.03.098
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10754
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Procedia Computer Science
dc.relation.references.spa.fl_str_mv [1] Kamatkar, S. J., Kamble, A., Viloria, A., Hernández-Fernandez, L., & Cali, E. G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.
[2] Saias, J.: Sentiue: Target and aspect-based sentiment analysis in semeval-2015 task 12. In: Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, Colorado, Association for Computational Linguistics, pp. 767–771 (2015)
[3] Sanzón, Y. M., Vilariño, D., Somodevilla, M. J., Zepeda, C., Tovar, M.: Modelos para detectar la polaridad de los mensajes en redes sociales. Research in Computing Science, 99, pp. 29–42 (2015)
[4] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., Portillo-Medina 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
[5] 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.
[6] Viloria, A., & Lezama, O. B. P. (2019). Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. Procedia Computer Science, 151, 1201-1206.
[7] Viloria, A., Acuña, G. C., Franco, D. J. A., Hernández-Palma, H., Fuentes, J. P., & Rambal, E. P. (2019). Integration of Data Mining Techniques to PostgreSQL Database Manager System. Procedia Computer Science, 155, 575-580
[8] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, Portland, Oregon, USA, pp. 151–160 (2011)
[9] Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase level sentiment analysis. In: HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Vancouver, British Columbia, Canada (2005)
[10] Bakliwal, A., Foster, J., van der Puil, J., OBrien, R., Tounsi, L., Hughes, M.: Sentiment analysis of political tweets: Towards an accurate classifier. In: Proceedings of the Workshop on Language in Social Media, Atlanta, Georgia, Association for Computational Linguistics, pp.49–58 (2013)
[11] Khan, F. H., Bashir, S., Qamar, U.: Tom: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, pp. 245–257 (2014)
[12] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL- Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jiménez, S. M., Eryigit, G.: Semeval-2018 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, California, Association for Computational Linguistics, pp. 19–30 (2018)
[13] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, pp. 2825–2830 (2011)
[14] Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit- learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
[15] Viloria, A., & Gaitan-Angulo, M. (2018). 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/2018/v9i47/107371.
[16] 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.
[17] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80–83, 1945.
[18] Martín-Wanton, Tamara, and Aurora Pons-Porrata. "Opinion polarity detection-using word sense disambiguation to determine the polarity of opinions." International Conference on Agents and Artificial Intelligence. Vol. 2. SCITEPRESS, 2010.
[19] Hercig, T., Brychcín, T., Svoboda, L., Konkol, M.: Uwb at semeval-2018 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, California, Association for Computational Linguistics, pp. 354– 361 (2018)
[20] Deng, Z. H., Luo, K. H., Yu, H. L.: A study of supervised term weighting scheme for sentiment analysis. Expert Systems with Applications, 41, pp. 3506–3513 (2014)
[21] Peñalver, I., Garcia, F., Valencia, R., Rodríguez, M. A., Moreno, V., Fraga, A., Sánchez, J. L.: Feature-based opinion mining through ontologies. Expert Systems with Applications, 41, pp. 5995–6008 (2014)
[22] Balaguer, E. V., Rosso, P., Locoro, A., Mascardi, V.: Análisis de opiniones con ontologıas. Polibits, 41, pp. 29–36 (2010)
[23] Vairetti, Carla, et al. "Enhancing the classification of social media opinions by optimizing the structural information." Future Generation Computer Systems 102 (2020): 838-846.
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© Copyright 2020 Elsevier B.V., All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Silva, JesusVargas, JesusCabrera, DanelysGarcía, PatriciaBonerge Pineda, OmarMarin Gonzalez, FredyVargas Mercado, Carlos2024-02-21T13:59:09Z2024-02-21T13:59:09Z2020-04-09Jesus Silva, Jesus Vargas, Danelys Cabrera, Patricia García, Omar Bonerge Pineda, Fredy Marin Gonzalez, Carlos Vargas Mercado, Algorithm for Detecting Opinion Polarity in Laptop and Restaurant Domains, Procedia Computer Science,Volume 170, 2020,Pages 977-982, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.098.1877-0509https://hdl.handle.net/11323/1075410.1016/j.procs.2020.03.098Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Knowing the state of public opinion regarding a product, the reputation of a celebrity or the assessment of a political candidate is very useful in today’s world. For example, in the particular case of a company, analyzing the opinions of its customers is very beneficial, since it allows to know the disconformities and, from these, to define new strategies that guarantee to obtain a greater degree of satisfaction in the users, as well as a greater success within the market. This article aims to develop a method for classifying the polarity of product aspects.6 páginasapplication/pdfengElsevier BVNetherlandshttps://www.scopus.com/record/display.uri?eid=2-s2.0-85085582463&doi=10.1016%2fj.procs.2020.03.098&origin=inward&txGid=a832eeb507cf35bb83be0236069a5163Algorithm for Detecting Opinion Polarity in Laptop and Restaurant DomainsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Procedia Computer Science[1] Kamatkar, S. J., Kamble, A., Viloria, A., Hernández-Fernandez, L., & Cali, E. G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.[2] Saias, J.: Sentiue: Target and aspect-based sentiment analysis in semeval-2015 task 12. In: Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, Colorado, Association for Computational Linguistics, pp. 767–771 (2015)[3] Sanzón, Y. M., Vilariño, D., Somodevilla, M. J., Zepeda, C., Tovar, M.: Modelos para detectar la polaridad de los mensajes en redes sociales. Research in Computing Science, 99, pp. 29–42 (2015)[4] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., Portillo-Medina 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[5] 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.[6] Viloria, A., & Lezama, O. B. P. (2019). Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. Procedia Computer Science, 151, 1201-1206.[7] Viloria, A., Acuña, G. C., Franco, D. J. A., Hernández-Palma, H., Fuentes, J. P., & Rambal, E. P. (2019). Integration of Data Mining Techniques to PostgreSQL Database Manager System. Procedia Computer Science, 155, 575-580[8] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, Portland, Oregon, USA, pp. 151–160 (2011)[9] Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase level sentiment analysis. In: HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Vancouver, British Columbia, Canada (2005)[10] Bakliwal, A., Foster, J., van der Puil, J., OBrien, R., Tounsi, L., Hughes, M.: Sentiment analysis of political tweets: Towards an accurate classifier. In: Proceedings of the Workshop on Language in Social Media, Atlanta, Georgia, Association for Computational Linguistics, pp.49–58 (2013)[11] Khan, F. H., Bashir, S., Qamar, U.: Tom: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, pp. 245–257 (2014)[12] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL- Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jiménez, S. M., Eryigit, G.: Semeval-2018 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, California, Association for Computational Linguistics, pp. 19–30 (2018)[13] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, pp. 2825–2830 (2011)[14] Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit- learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)[15] Viloria, A., & Gaitan-Angulo, M. (2018). 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/2018/v9i47/107371.[16] 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.[17] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, vol. 1, no. 6, pp. 80–83, 1945.[18] Martín-Wanton, Tamara, and Aurora Pons-Porrata. "Opinion polarity detection-using word sense disambiguation to determine the polarity of opinions." International Conference on Agents and Artificial Intelligence. Vol. 2. SCITEPRESS, 2010.[19] Hercig, T., Brychcín, T., Svoboda, L., Konkol, M.: Uwb at semeval-2018 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, California, Association for Computational Linguistics, pp. 354– 361 (2018)[20] Deng, Z. H., Luo, K. H., Yu, H. L.: A study of supervised term weighting scheme for sentiment analysis. Expert Systems with Applications, 41, pp. 3506–3513 (2014)[21] Peñalver, I., Garcia, F., Valencia, R., Rodríguez, M. A., Moreno, V., Fraga, A., Sánchez, J. L.: Feature-based opinion mining through ontologies. Expert Systems with Applications, 41, pp. 5995–6008 (2014)[22] Balaguer, E. V., Rosso, P., Locoro, A., Mascardi, V.: Análisis de opiniones con ontologıas. Polibits, 41, pp. 29–36 (2010)[23] Vairetti, Carla, et al. "Enhancing the classification of social media opinions by optimizing the structural information." Future Generation Computer Systems 102 (2020): 838-846.982977170PolarityLatent semantic analysisSurface featureParse featurePublicationORIGINALAlgorithm for Detecting Opinion Polarity in Laptop and Restaurant.pdfAlgorithm for Detecting Opinion Polarity in Laptop and Restaurant.pdfArtículoapplication/pdf606677https://repositorio.cuc.edu.co/bitstreams/a67bc5f3-da9d-44c6-b23a-bbe57f2db793/downloada47bc5e9afd5dcf08cfe20a059c9c014MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/328a6e99-6945-47b7-ae24-4f7842fc65ba/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTAlgorithm for Detecting Opinion Polarity in Laptop and Restaurant.pdf.txtAlgorithm for Detecting Opinion Polarity in Laptop and Restaurant.pdf.txtExtracted texttext/plain27090https://repositorio.cuc.edu.co/bitstreams/26ed650b-2d81-4fbd-873a-3ddfbb036e41/download770144cd1076c657ed1bca2a5fffb67fMD53THUMBNAILAlgorithm for Detecting Opinion Polarity in Laptop and 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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.
