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Prototipo de interfaz interactiva de análisis de emociones en textos

dc.contributor.advisorLópez López, Juan Manuel
dc.contributor.advisorPineda Vargas, Mónica Patricia
dc.creatorOlivares Cortés, Juan Sebastián
dc.creator.degreeIngeniero Biomédicospa
dc.creator.degreetypePart timespa
dc.date.accessioned2021-05-31T19:38:47Z
dc.date.available2021-05-31T19:38:47Z
dc.date.created2021-05-26
dc.descriptionPara 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.spa
dc.description.abstractFor 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.spa
dc.format.extent47 pp.spa
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_31536
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/31536
dc.language.isospaspa
dc.publisherUniversidad del Rosariospa
dc.publisher.departmentEscuela de Medicina y Ciencias de la Saludspa
dc.publisher.programIngeniería Biomédicaspa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.rights.licenciaEL 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.spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.source.bibliographicCitationWIRE, 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].spa
dc.source.bibliographicCitationMadhavan, 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].spa
dc.source.bibliographicCitationMedium. 2021. Medium. [online] Available at: <https://becominghuman.ai/alternative-nlpmethod-9f94165802ed> [Accessed 10 April 2021].spa
dc.source.bibliographicCitationEnterprisersproject.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]spa
dc.source.bibliographicCitationBeccue, 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].spa
dc.source.bibliographicCitationMarket, 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]spa
dc.source.bibliographicCitationB. 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].spa
dc.source.bibliographicCitationN. 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/s10115-020-01449-0spa
dc.source.bibliographicCitationThe 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]spa
dc.source.bibliographicCitationGendron 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: 10.1177/1754073909338877spa
dc.source.bibliographicCitationD. 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: 10.1080/09658210903130764spa
dc.source.bibliographicCitationJ. 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/s0954579405050340spa
dc.source.bibliographicCitationJ. 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]spa
dc.source.bibliographicCitationInvestigating the Physiology of Human Decision-Making | Dana Foundation", Dana Foundation, 2021. [Online]. Available: https://www.dana.org/grant/investigating-thephysiology-of-human-decision-making/. [Accessed: 03- May- 2021]spa
dc.source.bibliographicCitationFloydHub Blog. 2021. Tokenizers: How machines read. [online] Available at: <https://blog.floydhub.com/tokenization-nlp/> [Accessed 12 April 2021]spa
dc.source.bibliographicCitationY. Zhang, R. Jin and Z. Zhou, "Understanding bag-of-words model: a statistical framework", International Journal of Machine Learning and Cybernetics, vol. 1, no. 1-4, pp. 43-52, 2010. Available: 10.1007/s13042-010-0001-0 [Accessed 10 May 2021].spa
dc.source.bibliographicCitationJ. Ramos, “Using tf-idf to determine word relevance in document queries,” 01 2003spa
dc.source.bibliographicCitationD. Jurafsky and J. Martin, Speech and language processing. Uttar Pradesh (India): Pearson, 2020spa
dc.source.bibliographicCitationS. Ruder, “Nlp’s imagenet moment has arrived,”The Gradient, 2018. [online] Available at: <https://blog.floydhub.com/tokenization-nlp/> [Accessed 12 April 2021spa
dc.source.bibliographicCitationM. 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/1802.05365spa
dc.source.bibliographicCitationAlammar, 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]spa
dc.source.bibliographicCitationA. Radford and K. Narasimhan, “Improving language understanding by generative pretraining,” 2018spa
dc.source.bibliographicCitationJ. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT:Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of theNorth American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume1(LongandShortPapers).Minneapolis,Minnesota:AssociationforComp utational Linguistics, Jun. 2019, pp. 4171–4186. [Online].Available: https://www.aclweb.org/anthology/N19-1423spa
dc.source.bibliographicCitationA.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/1804.07461spa
dc.source.bibliographicCitationLatysheva, N., 2019. 2019: The Year of BERT. [online] Medium. Available at: <https://towardsdatascience.com/2019-the-year-of-bert-354e8106f7ba> [Accessed 13 April 2021]spa
dc.source.bibliographicCitationD. Kondratyuk, T. Gavenciak, M. Straka, and J. Hajic, “Lemmatag:Jointly tagging and lemmatizing for morphologically-rich languages with BRRNs,”CoRR, vol. abs/1808.03703, 2018. [Online]. Available:http://arxiv.org/abs/1808.03703spa
dc.source.bibliographicCitationS. Buechel and U. Hahn, "EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis", Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2017. Available: 10.18653/v1/e17-2092 [Accessed 3 May 2021].spa
dc.source.bibliographicCitationS. 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].spa
dc.source.bibliographicCitationS. Mohammad and F. Bravo-Marquez, "WASSA-2017 Shared Task on Emotion Intensity", arXiv.org, 2021. [Online]. Available: https://arxiv.org/abs/1708.03700. [Accessed: 03- May- 2021]spa
dc.source.bibliographicCitationP. Govindaraj, "Emotions dataset for NLP", Kaggle.com, 2021. [Online]. Available: https://www.kaggle.com/praveengovi/emotions-dataset-for-nlp. [Accessed: 03- May- 2021].spa
dc.source.bibliographicCitationE. Saravia, H. Liu, Y. Huang, J. Wu and Y. Chen, "CARER: Contextualized Affect Representations for Emotion Recognition", Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018. Available: 10.18653/v1/d18-1404 [Accessed 3 May 2021]spa
dc.source.bibliographicCitationD. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade and S. Ravi, "GoEmotions: A Dataset of Fine-Grained Emotions", arXiv.org, 2020. [Online]. Available: https://arxiv.org/abs/2005.00547. [Accessed: 03- May- 2021].spa
dc.source.bibliographicCitationF. Acheampong, C. Wenyu and H. Nunoo‐Mensah, "Text‐based emotion detection: Advances, challenges, and opportunities", Engineering Reports, vol. 2, no. 7, 2020. Available: 10.1002/eng2.12189 [Accessed 3 May 2021].spa
dc.source.bibliographicCitationA. Uysal and S. Gunal, "The impact of preprocessing on text classification", Information Processing & Management, vol. 50, no. 1, pp. 104-112, 2014. Available: 10.1016/j.ipm.2013.08.006 [Accessed 3 May 2021].spa
dc.source.bibliographicCitationC. Huang, A. Trabelsi and O. Zaïane, "ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT", Proceedings of the 13th International Workshop on Semantic Evaluation, 2019. Available: 10.18653/v1/s19-2006 [Accessed 3 May 2021].spa
dc.source.bibliographicCitationBase Words and Infectional Ending", Institute of Education Sciences. [Online]. Available: https://ies.ed.gov/ncee/edlabs/regions/southeast/foundations/resources/secondgrade/rec3/ 3.3_Act_17_Base_Words_and_Inflectional_Endings.pdf. [Accessed: 03- May- 2021]spa
dc.source.bibliographicCitationStemming and lemmatization, Nlp.stanford.edu, 2008. [Online]. Available: https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html. [Accessed: 03- May- 2021].spa
dc.source.bibliographicCitationL. Vallantin, "Why is removing stop words not always a good idea", Medium, 2019. [Online]. Available: https://medium.com/@limavallantin/why-is-removing-stop-words-notalways-a-good-idea-c8d35bd77214. [Accessed: 03- May- 2021]spa
dc.source.bibliographicCitationJ. Ma and D. Yarats, "On the adequacy of untuned warmup for adaptive optimization", arXiv.org, 2021. [Online]. Available: https://arxiv.org/abs/1910.04209. [Accessed: 03- May2021]spa
dc.source.bibliographicCitationM. Grandini, E. Bagli and G. Visani, "Metrics for Multi-Class Classification: an Overview", arXiv.org, 2020. [Online]. Available: https://arxiv.org/abs/2008.05756. [Accessed: 03- May2021].spa
dc.source.bibliographicCitationJ. J, "MAE and RMSE — Which Metric is Better?", Medium, 2016. [Online]. Available: https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-bettere60ac3bde13d. [Accessed: 11- May- 2021].spa
dc.source.bibliographicCitationY. Huang, S. Lee, M. Ma, Y. Chen, Y. Yu and Y. Chen, "EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation", arXiv.org, 2019. [Online]. Available: http://arxiv.org/abs/1908.06264. [Accessed: 12- May- 2021]spa
dc.source.bibliographicCitationJ. López et al., "Induced EEG activity during the IAPS tests and avEMT in intimate partner violence against women", 14th International Symposium on Medical Information Processing and Analysis, 2018. Available: 10.1117/12.2511600 [Accessed 23 May 2021].spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subjectSistema de inteligencia artificial para interpretar emocionesspa
dc.subjectPrototipo de interfaz interactiva de análisis de emociones en textosspa
dc.subjectSistema de inteligencia artificial para el procesamiento de lenguaje natural (PLN)spa
dc.subjectSistemas de inteligencia artificial programado con Red neuronal basada en la arquitectura transformer BERTspa
dc.subject.ddcMétodos especiales de computaciónspa
dc.subject.keywordArtificial intelligence system to interpret emotionsspa
dc.subject.keywordInteractive interface prototype for analyzing emotions in textsspa
dc.subject.keywordArtificial intelligence system for natural language processing (NLP)spa
dc.subject.keywordArtificial intelligence systems programmed with a neural network based on the BERT transformer architecturespa
dc.titlePrototipo de interfaz interactiva de análisis de emociones en textosspa
dc.title.TranslatedTitleInteractive interface prototype for analyzing emotions in textseng
dc.typebachelorThesiseng
dc.type.documentMonografíaspa
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de gradospa
local.department.reportEscuela de Medicina y Ciencias de la Saludspa
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