Prototipo de interfaz interactiva de análisis de emociones en textos

Para los seres humanos, entender las emociones no es una tarea fácil y a menudo es muy complejo describir, interpretar y evaluar las emociones que estamos sintiendo. Reconocer las emociones es de vital importancia debido a que este proceso está relacionado con la toma de decisiones lo cual continuam...

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Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
spa
OAI Identifier:
oai:repository.urosario.edu.co:10336/31536
Acceso en línea:
https://doi.org/10.48713/10336_31536
https://repository.urosario.edu.co/handle/10336/31536
Palabra clave:
Sistema de inteligencia artificial para interpretar emociones
Prototipo de interfaz interactiva de análisis de emociones en textos
Sistema de inteligencia artificial para el procesamiento de lenguaje natural (PLN)
Sistemas de inteligencia artificial programado con Red neuronal basada en la arquitectura transformer BERT
Métodos especiales de computación
Artificial intelligence system to interpret emotions
Interactive interface prototype for analyzing emotions in texts
Artificial intelligence system for natural language processing (NLP)
Artificial intelligence systems programmed with a neural network based on the BERT transformer architecture
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Atribución-NoComercial-CompartirIgual 2.5 Colombia
id EDOCUR2_369675552e52f2a18be0e2017b2322b2
oai_identifier_str oai:repository.urosario.edu.co:10336/31536
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.spa.fl_str_mv Prototipo de interfaz interactiva de análisis de emociones en textos
dc.title.TranslatedTitle.eng.fl_str_mv Interactive interface prototype for analyzing emotions in texts
title Prototipo de interfaz interactiva de análisis de emociones en textos
spellingShingle Prototipo de interfaz interactiva de análisis de emociones en textos
Sistema de inteligencia artificial para interpretar emociones
Prototipo de interfaz interactiva de análisis de emociones en textos
Sistema de inteligencia artificial para el procesamiento de lenguaje natural (PLN)
Sistemas de inteligencia artificial programado con Red neuronal basada en la arquitectura transformer BERT
Métodos especiales de computación
Artificial intelligence system to interpret emotions
Interactive interface prototype for analyzing emotions in texts
Artificial intelligence system for natural language processing (NLP)
Artificial intelligence systems programmed with a neural network based on the BERT transformer architecture
title_short Prototipo de interfaz interactiva de análisis de emociones en textos
title_full Prototipo de interfaz interactiva de análisis de emociones en textos
title_fullStr Prototipo de interfaz interactiva de análisis de emociones en textos
title_full_unstemmed Prototipo de interfaz interactiva de análisis de emociones en textos
title_sort Prototipo de interfaz interactiva de análisis de emociones en textos
dc.contributor.advisor.none.fl_str_mv López López, Juan Manuel
Pineda Vargas, Mónica Patricia
dc.subject.spa.fl_str_mv Sistema de inteligencia artificial para interpretar emociones
Prototipo de interfaz interactiva de análisis de emociones en textos
Sistema de inteligencia artificial para el procesamiento de lenguaje natural (PLN)
Sistemas de inteligencia artificial programado con Red neuronal basada en la arquitectura transformer BERT
topic Sistema de inteligencia artificial para interpretar emociones
Prototipo de interfaz interactiva de análisis de emociones en textos
Sistema de inteligencia artificial para el procesamiento de lenguaje natural (PLN)
Sistemas de inteligencia artificial programado con Red neuronal basada en la arquitectura transformer BERT
Métodos especiales de computación
Artificial intelligence system to interpret emotions
Interactive interface prototype for analyzing emotions in texts
Artificial intelligence system for natural language processing (NLP)
Artificial intelligence systems programmed with a neural network based on the BERT transformer architecture
dc.subject.ddc.spa.fl_str_mv Métodos especiales de computación
dc.subject.keyword.spa.fl_str_mv Artificial intelligence system to interpret emotions
Interactive interface prototype for analyzing emotions in texts
Artificial intelligence system for natural language processing (NLP)
Artificial intelligence systems programmed with a neural network based on the BERT transformer architecture
description Para los seres humanos, entender las emociones no es una tarea fácil y a menudo es muy complejo describir, interpretar y evaluar las emociones que estamos sintiendo. Reconocer las emociones es de vital importancia debido a que este proceso está relacionado con la toma de decisiones lo cual continuamente nos lleva a evaluar situaciones e identificar potenciales fuentes de conflicto y tener control al iniciar la respuesta a dichas acciones con el fin de resolver el conflicto y generar una respuesta adecuada frente al estímulo. Por otra parte, el procesamiento de lenguaje natural busca brindar a los computadores las herramientas para entender, interpretar y manipular el lenguaje humano a partir del reconocimiento de patrones. En este proyecto dirigido, se desarrolla un prototipo de interfaz interactiva de análisis de emociones en textos, en el en el cual se propone y aplica una metodología que permite la utilización de diversas bases de datos de textos, para la detección de emociones básicas, utilizando técnicas de PLN y su integración con una interfaz gráfica de usuario que indica gráficamente parámetros de valencia y activación emocional. Además se genera un modelo de inteligencia artificial a partir del ajuste fino de la arquitectura BERT para la clasificación de 4 emociones (alegría,calma,ira y tristeza) dadas por el modelo circumplejo de emociones de Russell con un valor F1 ponderado de 79%. Finalmente, se integra el modelo de inteligencia artificial en una interfaz web en un servidor local para su uso.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-05-31T19:38:47Z
dc.date.available.none.fl_str_mv 2021-05-31T19:38:47Z
dc.date.created.none.fl_str_mv 2021-05-26
dc.type.eng.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.spa.fl_str_mv Monografía
dc.type.spa.spa.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_31536
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/31536
url https://doi.org/10.48713/10336_31536
https://repository.urosario.edu.co/handle/10336/31536
dc.language.iso.spa.fl_str_mv spa
language spa
dc.rights.*.fl_str_mv Atribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/co/
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 2.5 Colombia
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-sa/2.5/co/
http://purl.org/coar/access_right/c_abf2
dc.format.extent.spa.fl_str_mv 47 pp.
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad del Rosario
dc.publisher.department.spa.fl_str_mv Escuela de Medicina y Ciencias de la Salud
dc.publisher.program.spa.fl_str_mv Ingeniería Biomédica
institution Universidad del Rosario
dc.source.bibliographicCitation.spa.fl_str_mv WIRE, B., 2021. Global Revenue for Enterprise Applications is Expected to Reach $107.3 Billion by 2025, as Enterprises Move From Trials to Deployments, According to Tractica. [online] Businesswire.com. Available at: <https://www.businesswire.com/news/home/20191014005213/en/Global-Revenue-forEnterprise-Applications-is-Expected-to-Reach-107.3-Billion-by-2025-as-Enterprises-MoveFrom-Trials-to-Deployments-According-to-Tractica> [Accessed 12 April 2021].
Madhavan, R., 2021. Natural Language Processing - Current Applications and Future Possibilities. [online] Emerj. Available at: <https://emerj.com/partner-content/nlp-currentapplications-and-future-possibilities/> [Accessed 12 April 2021].
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Beccue, M. and Kaul, A., 2021. Tractica Report: Natural Language Processing for the Enterprise. [online] IT Pro. Available at: <https://www.itprotoday.com/artificialintelligence/tractica-report-natural-language-processing-enterprise> [Accessed 10 April 2021].
Market, N., 2021. Natural Language Processing Market Size, Share and Global Market Forecast to 2026 | MarketsandMarkets. [online] Marketsandmarkets.com. Available at: <https://www.marketsandmarkets.com/Market-Reports/natural-language-processing-nlp825.html> [Accessed 12 April 2021]
B. WIRE, "Natural Language Processing Market to Reach $22.3 Billion by 2025, According to Tractica", Businesswire.com, 2021. [Online]. Available: https://www.businesswire.com/news/home/20170821005088/en/Natural-LanguageProcessing-Market-to-Reach-22.3-Billion-by-2025-According-to-Tractica. [Accessed: 03- May- 2021].
N. Alswaidan and M. Menai, "A survey of state-of-the-art approaches for emotion recognition in text", Knowledge and Information Systems, vol. 62, no. 8, pp. 2937-2987, 2020. Available: 10.1007/s10115-020-01449-0
The Passion of the Soul - Early Emotion Theories", Sagepub.com, 2021. [Online]. Available: https://www.sagepub.com/sites/default/files/upmbinaries/63133_Schirmer_Chapter_1.pdf. [Accessed: 03- May- 2021]
Gendron and L. Feldman Barrett, "Reconstructing the Past: A Century of Ideas About Emotion in Psychology", Emotion Review, vol. 1, no. 4, pp. 316-339, 2009. Available: 10.1177/1754073909338877
D. Rubin and J. Talarico, "A comparison of dimensional models of emotion: Evidence from emotions, prototypical events, autobiographical memories, and words", Memory, vol. 17, no. 8, pp. 802-808, 2009. Available: 10.1080/09658210903130764
J. POSNER, J. RUSSELL and B. PETERSON, "The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology", Development and Psychopathology, vol. 17, no. 03, 2005. Available: 10.1017/s0954579405050340
J. Lerner, Y. Li, P. Valdesolo and K. Kassam, "Emotion and Decision Making", Annual Review of Psychology, vol. 66, no. 1, pp. 799-823, 2015. Available: 10.1146/annurev-psych010213-115043 [Accessed 3 May 2021]
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M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark,K.Lee,andL.Zettlemoyer,“Deep Contextualized Word Representations,”CoRR, vol. abs/1802.05365, 2018. [Online]. Available:http://arxiv.org/abs/1802.05365
Alammar, J., 2018. The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning). [online] Jalammar.github.io. Available at: <http://jalammar.github.io/illustratedbert/> [Accessed 12 April 2021]
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A.Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman,“GLUE: A multi-task benchmark and analysis platform for natural language understanding,”CoRR, vol. abs/1804.07461, 2018. [Online].Available: http://arxiv.org/abs/1804.07461
Latysheva, N., 2019. 2019: The Year of BERT. [online] Medium. Available at: <https://towardsdatascience.com/2019-the-year-of-bert-354e8106f7ba> [Accessed 13 April 2021]
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S. Mohammad, F. Bravo-Marquez, M. Salameh and S. Kiritchenko, "SemEval-2018 Task 1: Affect in Tweets", Proceedings of The 12th International Workshop on Semantic Evaluation, 2018. Available: 10.18653/v1/s18-1001 [Accessed 3 May 2021].
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spelling López López, Juan Manuela34ddbcd-d51d-4e40-a61e-3baa9d32826d600Pineda Vargas, Mónica Patriciac2ff2615-73ed-4e24-8d3f-4791f170514c600Olivares Cortés, Juan SebastiánIngeniero BiomédicoPart time0f7afdbb-5ab8-4d3f-b0bc-112c9258520b6002021-05-31T19:38:47Z2021-05-31T19:38:47Z2021-05-26Para los seres humanos, entender las emociones no es una tarea fácil y a menudo es muy complejo describir, interpretar y evaluar las emociones que estamos sintiendo. Reconocer las emociones es de vital importancia debido a que este proceso está relacionado con la toma de decisiones lo cual continuamente nos lleva a evaluar situaciones e identificar potenciales fuentes de conflicto y tener control al iniciar la respuesta a dichas acciones con el fin de resolver el conflicto y generar una respuesta adecuada frente al estímulo. Por otra parte, el procesamiento de lenguaje natural busca brindar a los computadores las herramientas para entender, interpretar y manipular el lenguaje humano a partir del reconocimiento de patrones. En este proyecto dirigido, se desarrolla un prototipo de interfaz interactiva de análisis de emociones en textos, en el en el cual se propone y aplica una metodología que permite la utilización de diversas bases de datos de textos, para la detección de emociones básicas, utilizando técnicas de PLN y su integración con una interfaz gráfica de usuario que indica gráficamente parámetros de valencia y activación emocional. Además se genera un modelo de inteligencia artificial a partir del ajuste fino de la arquitectura BERT para la clasificación de 4 emociones (alegría,calma,ira y tristeza) dadas por el modelo circumplejo de emociones de Russell con un valor F1 ponderado de 79%. Finalmente, se integra el modelo de inteligencia artificial en una interfaz web en un servidor local para su uso.For human beings, understanding emotions is not an easy task and it is often very complex to describe, interpret and evaluate the emotions we are feeling. Recognizing emotions is really important because this process is related to decision-making, which continually leads us to evaluate situations and identify possible sources of conflict and have control when initiating the response to those actions in order to resolve the conflict and generate an adequate response to the stimulus. On the other hand, natural language processing seeks to provide computers with the tools to understand, interpret and manipulate human language based on pattern recognition. In this project, a prototype of an interactive interface for emotion analysis in texts is developed, also a methodology is proposed and applied that allows the use of severarl text databases for the detection of basic emotions using NLP techniques and their integration with a graphical user interface that graphically indicates valence and emotional activation parameters. In addition, an artificial intelligence model is generated from the fine tuning of the BERT architecture for the classification of 4 emotions (joy, calm, anger and sadness) given by Russell's circumplex model of emotions with a weighted F1 score of 79%. Finally, the artificial intelligence model is integrated into a web interface on a local server for its use.47 pp.application/pdfhttps://doi.org/10.48713/10336_31536 https://repository.urosario.edu.co/handle/10336/31536spaUniversidad del RosarioEscuela de Medicina y Ciencias de la SaludIngeniería BiomédicaAtribución-NoComercial-CompartirIgual 2.5 ColombiaAbierto (Texto Completo)EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma.http://creativecommons.org/licenses/by-nc-sa/2.5/co/http://purl.org/coar/access_right/c_abf2WIRE, B., 2021. Global Revenue for Enterprise Applications is Expected to Reach $107.3 Billion by 2025, as Enterprises Move From Trials to Deployments, According to Tractica. [online] Businesswire.com. Available at: <https://www.businesswire.com/news/home/20191014005213/en/Global-Revenue-forEnterprise-Applications-is-Expected-to-Reach-107.3-Billion-by-2025-as-Enterprises-MoveFrom-Trials-to-Deployments-According-to-Tractica> [Accessed 12 April 2021].Madhavan, R., 2021. Natural Language Processing - Current Applications and Future Possibilities. [online] Emerj. Available at: <https://emerj.com/partner-content/nlp-currentapplications-and-future-possibilities/> [Accessed 12 April 2021].Medium. 2021. Medium. [online] Available at: <https://becominghuman.ai/alternative-nlpmethod-9f94165802ed> [Accessed 10 April 2021].Enterprisersproject.com. 2021. Artificial intelligence (AI) vs. natural language processing (NLP): What are the differences?. [online] Available at: <https://enterprisersproject.com/article/2020/2/artificial-intelligence-ai-vs-natural-languageprocessing-nlp-differences> [Accessed 10 April 2021]Beccue, M. and Kaul, A., 2021. Tractica Report: Natural Language Processing for the Enterprise. [online] IT Pro. Available at: <https://www.itprotoday.com/artificialintelligence/tractica-report-natural-language-processing-enterprise> [Accessed 10 April 2021].Market, N., 2021. Natural Language Processing Market Size, Share and Global Market Forecast to 2026 | MarketsandMarkets. [online] Marketsandmarkets.com. Available at: <https://www.marketsandmarkets.com/Market-Reports/natural-language-processing-nlp825.html> [Accessed 12 April 2021]B. WIRE, "Natural Language Processing Market to Reach $22.3 Billion by 2025, According to Tractica", Businesswire.com, 2021. [Online]. Available: https://www.businesswire.com/news/home/20170821005088/en/Natural-LanguageProcessing-Market-to-Reach-22.3-Billion-by-2025-According-to-Tractica. [Accessed: 03- May- 2021].N. Alswaidan and M. Menai, "A survey of state-of-the-art approaches for emotion recognition in text", Knowledge and Information Systems, vol. 62, no. 8, pp. 2937-2987, 2020. Available: 10.1007/s10115-020-01449-0The Passion of the Soul - Early Emotion Theories", Sagepub.com, 2021. [Online]. Available: https://www.sagepub.com/sites/default/files/upmbinaries/63133_Schirmer_Chapter_1.pdf. [Accessed: 03- May- 2021]Gendron and L. Feldman Barrett, "Reconstructing the Past: A Century of Ideas About Emotion in Psychology", Emotion Review, vol. 1, no. 4, pp. 316-339, 2009. Available: 10.1177/1754073909338877D. Rubin and J. Talarico, "A comparison of dimensional models of emotion: Evidence from emotions, prototypical events, autobiographical memories, and words", Memory, vol. 17, no. 8, pp. 802-808, 2009. Available: 10.1080/09658210903130764J. POSNER, J. RUSSELL and B. PETERSON, "The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology", Development and Psychopathology, vol. 17, no. 03, 2005. Available: 10.1017/s0954579405050340J. Lerner, Y. Li, P. Valdesolo and K. 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