Modelo de aprendizaje automático para la clasificación temprana de flujos de texto aplicado a la detección de desórdenes psicológicos

dc.contributorAugusto González, Fabio
dc.contributorMindlab
dc.creatorBabativa Melgarejo, Diego Alejandro
dc.date.accessioned2022-02-17T21:33:06Z
dc.date.available2022-02-17T21:33:06Z
dc.date.created2022-02-17T21:33:06Z
dc.date.issued2021
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81008
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLa representación adecuada de los flujos de textos en un modelo de aprendizaje automático permite la acumulación efectiva de evidencia secuencial, donde los algoritmos toman la decisión de clasificación cuando hay suficiente certeza para determinar la existencia de cierto tipo de riesgo. Lo que resulta determinante en la detección temprana de trastornos mentales con tendencia al suicidio. Inspirado en lo anterior, el presente trabajo de investigación toma por objeto la realización de un modelo de aprendizaje automático efectivo en la detección de des ́ordenes psicológicos, como son la depresión, la anorexia y la autolesión; manifestados en los flujos de texto discriminados de publicaciones con caracterizaciones determinantes en la red social Reddit. El modelo establecido en esta tesis es entrenado por varios conjuntos de datos etiquetados por expertos del Conference and Labs of the Evaluation Forum (CLEF), dando lugar al establecimiento de una propuesta con menor n ́umero de escritos requeridos en la detección, sobresaliendo en la métrica ERDE y F1 en la identificación temprana de población con tendencia a la anorexia. (Texto tomado de la fuente)
dc.description.abstractThe adequate representation of text streams in a machine learning model allows the effective accumulation of sequential evidence, in which the algorithms make the classification decision when there is sufficient certainty to determine the existence of a certain type of risk. What is decisive in the early detection of mental disorders with a tendency to suicide. Inspired by the above, the present research work aims to carry out an effective machine learning model in the detection of psychological disorders, such as depression, anorexia and self-harm; mani- fested in the discriminated text streams of publications with decisive characterizations in the Reddit social network. The model established in this thesis is trained by several data sets labeled by experts from the Conference and Labs of the Evaluation Forum (CLEF), leading to the establishment of a proposal with a lower number of writings required in detection, excelling in the ERDE and F1 metrics in the early identification of a population with a tendency to anorexy.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherDepartamento de Ingeniería de Sistemas e Industrial
dc.publisherFacultad de Ingeniería
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relationRafael A. Calvo, David N. Milene, M. Sazzad Hussain, and Helen Christensen. Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5):649–685, 2017.
dc.relationAshok Malla, Ridha Joober, and Amparo Garcia. “Mental illness is like any other medical illness”: A critical examination of the statement and its impact on patient care and society, may 2015.
dc.relationYoshihiko Suhara, Yinzhan Xu, and Alex ’Sandy’ Pentland. Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In Proceedings of the 26th International Conference on World Wide Web, WWW ’17, page 715–724, Republic and Canton of Geneva, CHE, 2017. International World Wide Web Conferences Steering Committee.
dc.relationNicholas B Allen, Sarah E Hetrick, Julian G Simmons, and Ian B Hickie. Early intervention for depressive disorders in young people: the opportunity and the (lack of) evidence, 2007. This paper helps to justify the early detection problem.
dc.relationJuan S. Lara, Mario Ezra Aragón, Fabio A. González, and Manuel Montes-y Gómez. Deep Bag-of-Sub-Emotions for Depression Detection in Social Media. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12848 LNAI:60–72, 2021.
dc.relationDavid E. Losada, Fabio Crestani, and Javier Parapar. erisk 2017: Clef lab on early risk prediction on the internet: Experimental foundations. In Gareth J.F. Jones, Séamus Lawless, Julio Gonzalo, Liadh Kelly, Lorraine Goeuriot, Thomas Mandl, Linda Cappellato, and Nicola Ferro, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction, pages 346–360, Cham, 2017. Springer International Publishing.
dc.relationDavid E Losada and Fabio Crestani. A Test Collection for Research on Depression and Language Use CLEF 2016, Évora (Portugal). Experimental IR Meets Multilinguality, Multimodality, and Interaction, pages 28–29, 2016.
dc.relationMarcel Trotzek, Sven Koitka, and C. Friedrich. Word embeddings and linguistic metadata at the clef 2018 tasks for early detection of depression and anorexia. In CLEF, 2018.
dc.relationElena Fano. A comparative study of word embedding methods for early risk prediction on the internet. 2019.
dc.relationC. Aggarwal. [PDF] A Survey of Stream Classification Algorithms — Semantic Scholar, 2014.
dc.relationRodrigo Martínez-Castaño, Juan C Pichel, and David E Losada. A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media. International Journal of Environmental Research and Public Health Article.
dc.relationM S B Phridviraj and C V Gururao. ScienceDirect Data mining-past, present and future-a typical survey on data streams. Procedia Technology, 12:255–263, 2014.
dc.relationYu Bao Liu, Jia Rong Cai, Jian Yin, and Ada Wai Chee Fu. Clustering text data streams. Journal of Computer Science and Technology, 23:112–128, 1 2008.
dc.relationWei Xie, Feida Zhu, Jing Jiang, Ee-Peng Lim, and Ke Wang. Topicsketch: Real-time bursty topic detection from twitter. IEEE Transactions on Knowledge and Data Engineering, 28(8):2216–2229, 2016.
dc.relationRakshitha Godahewa, Trevor Yann, Christoph Bergmeir, and Francois Petitjean. Sea-sonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., jul 2020.
dc.relationClément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, and Manel Boumghar. How to detect novelty in textual data streams? a comparative study of existing methods. In Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Alexis Bondu, Thomas Guyet, and Romain Tavenard, editors, Advanced Analytics and Learning on Temporal Data, pages 110–125, Cham, 2020. Springer International Publishing.
dc.relationKostiantyn Kucher, Rafael M. Martins, Carita Paradis, and Andreas Kerren. Stance- Vis Prime: visual analysis of sentiment and stance in social media texts. Journal of Visualization, 23(6):1015–1034, dec 2020.
dc.relationIraklis Moutidis and Hywel T.P. Williams. Good and bad events: combining network- based event detection with sentiment analysis. Social Network Analysis and Mining, 10(1), dec 2020.
dc.relationAvishek Bose, Vahid Behzadan, Carlos Aguirre, and William H Hsu. A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams.
dc.relationDavid C. Mohr, Mi Zhang, and Stephen M. Schueller. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology, 13(1):23–47, may 2017.
dc.relationAdrien Guille, Cécile Favre, A Guille, and C Favre. Social Network Analysis and Mining Event detection, tracking and visualization in Twitter A mention-anomaly-based approach.
dc.relationWeiren Yu, Jianxin Li, Zakirul Alam Bhuiyan, Richong Zhang, and Jinpeng Huai. Ring: Real-Time Emerging Anomaly Monitoring System Over Text Streams. IEEE Transactions on Big Data, 5(4):506–519, feb 2017.
dc.relationNick Koudas, Nikos Sarkas, Michael Svendsen, Albert Angel, Divesh Srivastava, and Srikanta Tirthapura. ATT Labs-Research Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification.
dc.relationValentina Dragos, Bruce Forrester, and Kellyn Rein. Is hybrid AI suited for hybrid threats? Insights from social media analysis. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc., jul 2020.
dc.relationCarmen De Maio, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, and Alberto Volpe. Cross-relating heterogeneous Text Streams for Credibility Assessment. In IEEE Conference on Evolving and Adaptive Intelligent Systems, volume 2020-May. Institute of Electrical and Electronics Engineers Inc., may 2020.
dc.relationLoulwah Alsumait, Daniel Barbara, Carlotta Domeniconi, and Daniel Barbará. On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking Clustering Ensemble View project On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking.
dc.relationQiaozhu Mei and ChengXiang Zhai. Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD ’05, page 198–207, New York, NY, USA, 2005. Association for Computing Machinery.
dc.relationGe Song and Yunming Ye. A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance. 2014.
dc.relationZachary Miller, Brian Dickinson, and Wei Hu. Gender Prediction on Twitter Using Stream Algorithms with N-Gram Character Features. International Journal of Intelligence Science, 2:143–148, 2012.
dc.relationAnukarsh G. Prasad, S. Sanjana, Skanda M. Bhat, and B. S. Harish. Sentiment analysis for sarcasm detection on streaming short text data. In 2017 2nd International Conference on Knowledge Engineering and Applications, ICKEA 2017, volume 2017-Janua, pages 1–5. Institute of Electrical and Electronics Engineers Inc., dec 2017.
dc.relationM Trotzek, S Koitka, and C M Friedrich. Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences, 2018.
dc.relationJana M. Havigerová, Jirí Haviger, Dalibor Kucera, and Petra Hoffmannová. Text-based detection of the risk of depression. Frontiers in Psychology, 10:513, 2019.
dc.relationImed Zitouni. Natural language processing of semitic languages. 2014.
dc.relationYla R Tausczik and James W Pennebaker. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1):24–54, 2010.
dc.relationJudy Hanwen Shen and Frank Rudzicz. Detecting Anxiety through Reddit. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 58–65, Stroudsburg, PA, USA, 2017. Association for Computational Linguistics.
dc.relationKimia Hemmatirad, Hojjat Bagherzadeh, Ehsan Fazl-Ersi, and Abedin Vahedian. Detection of Mental Illness Risk on Social Media through Multi-level SVMs. pages 116–120. Institute of Electrical and Electronics Engineers (IEEE), oct 2020.
dc.relationSvitlana Volkova, Kyungsik Han, and Courtney Corley. Using social media to measure student wellbeing: A large-scale study of emotional response in academic discourse. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10046 LNCS:510–526, 11 2016.
dc.relationBridianne O’Dea, Stephen Wan, Philip J. Batterham, Alison L. Calear, Cecile Paris, and Helen Christensen. Detecting suicidality on twitter. Internet Interventions, 2(2):183–188, may 2015.
dc.relationMunmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. Predicting depression via social media. In ICWSM, 2013.
dc.relationStephanie S Rude, Eva-Maria Gortner, and James W Pennebaker. Language use of depressed and depression-vulnerable college students.
dc.relationExploring Eating Disorder Topics on Twitter: Machine Learning Approach. JMIR Medical Informatics, 8(10):e18273, oct 2020.
dc.relationDerek Howard, Marta M. Maslej, Justin Lee, Jacob Ritchie, Geoffrey Woollard, and Leon French. Transfer learning for risk classification of social media posts: Model evaluation study. Journal of Medical Internet Research, 22(5), may 2020.
dc.relationHaohan Wang and Bhiksha Raj. On the origin of deep learning on the origin of deep learning. 2017.
dc.relationElvis Saravia, Chun Hao Chang, Renaud Jollet De Lorenzo, and Yi Shin Chen. MIDAS: Mental illness detection and analysis via social media. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, pages 1418–1421. Institute of Electrical and Electronics Engineers Inc., nov 2016.
dc.relationKeumhee Kang, Chanhee Yoon, and Eun Yi Kim. Identifying depressive users in Twitter using multimodal analysis. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016, pages 231–238. Institute of Electrical and Electronics Engineers Inc., 2016.
dc.relationH. Andrew Schwartz, Maarten Sap, Margaret L. Kern, Johannes C. Eichstaedt, Adam Kapelner, Megha Agrawal, Eduardo Blanco, Lukasz Dziurzynski, Gregory Park, David Stillwell, Michal Kosinski, Martin E.P. Seligman, and Lyle H. Ungar. Predicting individual well-being through the language of social media. Pacific Symposium on Biocomputing, pages 516–527, 2016.
dc.relationStevie Chancellor, Zhiyuan Jerry Lin, Erica L. Goodman, Stephanie Zerwas, and Munmun De Choudhury. Quantifying and predicting mental illness severity in online proeating disorder communities. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, volume 27, pages 1171–1184. Association for Computing Machinery, feb 2016.
dc.relationScott R Braithwaite, Christophe Giraud-Carrier, Josh West, Michael D Barnes, ; Carl, Lee Hanson, and Carl Lee Hanson. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.
dc.relationGlen A. Coppersmith, Kim Ngo, Ryan Leary, and Anthony Wood. Exploratory analysis of social media prior to a suicide attempt. In CLPsych@HLT-NAACL, 2016.
dc.relationMeizhen Lv, Ang Li, Tianli Liu, and Tingshao Zhu. Creating a Chinese suicide dictionary for identifying suicide risk on social media. PeerJ, 2015(12):e1455, dec 2015.
dc.relationPete Burnap, Gualtiero Colombo, and Jonathan Scourfield. Machine classification and analysis of suicide-related communication on Twitter. In HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media, pages 75–84. Association for Computing Machinery, Inc, aug 2015.
dc.relationDaniel Preot ̧iuc-Pietro, Maarten Sap, H. Andrew Schwartz, and Lyle Ungar. Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task. pages 40–45. Association for Computational Linguistics (ACL), jul 2015.
dc.relationMargaret Mitchell, Kristy Hollingshead, and Glen Coppersmith. Quantifying the Language of Schizophrenia in Social Media. pages 11–20. Association for Computational Linguistics (ACL), jul 2015.
dc.relationPhilip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. pages 99–107. Association for Computational Linguistics (ACL), jul 2015.
dc.relationVíctor M. Prieto, Sérgio Matos, Manuel Álvarez, Fidel Cacheda, and José Luís Oliveira. Twitter: A good place to detect health conditions. PLoS ONE, 9(1), jan 2014.
dc.relationBibo Hao, Lin Li, Ang Li, and Tingshao Zhu. Predicting Mental Health Status on Social Media. pages 101–110. 2013.
dc.relationSho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, and Hiroyuki Ohsaki. Recognizing depression from twitter activity. In Conference on Human Factors in Computing Systems - Proceedings, volume 2015-April, pages 3187–3196. Association for Computing Machinery, apr 2015.
dc.relationHuijie Lin, Jia Jia, Quan Guo, Yuanyuan Xue, Qi Li, Jie Huang, Lianhong Cai, and Ling Feng. User-level psychological stress detection from social media using deep neural network. In MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, pages 507–516. Association for Computing Machinery, Inc, nov 2014.
dc.relationChristopher Homan, Ravdeep Johar, Tong Liu, Megan Lytle, Vincent Silenzio, and Cecilia Ovesdotter Alm. Toward macro-insights for suicide prevention: Analyzing finegrained distress at scale. pages 107–117. Association for Computational Linguistics (ACL), 6 2015.
dc.relationWaleed Ragheb, Bilel Moulahi, Jérôme Azé, Sandra Bringay, and Maximilien Servajean. Temporal Mood Variation: at the CLEF eRisk-2018 Tasks for Early Risk Detection on The Internet. In Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, volume 2125 of CEUR Workshop Proceedings, Avignon, France, September 2018.
dc.relationPiotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016.
dc.relationTomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed representations of words and phrases and their compositionality. CoRR, abs/1310.4546, 2013.
dc.relationMario Ezra Aragón, Adrian Pastor López-Monroy, Luis Carlos González-Gurrola, and Manuel Montes-y Gómez. Detecting depression in social media using fine-grained emotions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1481–1486, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.
dc.relationMatthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations, 2018.
dc.relationJacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pretraining of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.
dc.relationXingyou Wang, Weijie Jiang, and Zhiyong Luo. Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In COLING, 2016.
dc.relationFabian Pedregosa, Ga ̈el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay. Scikit-learn: Machine learning in python. ́ J. Mach. Learn. Res., 12(null):2825–2830, nov 2011.
dc.relationAdrian Pastor López-Monroy, Fabio A. Gonz ́alez, Manuel Montes, Hugo Jair Escalante, and Thamar Solorio. Early text classification using multi-resolution concept representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1216–1225, New Orleans, Louisiana, June 2018. Association for Computational Linguistics.
dc.relationF. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65:386–408, 11 1958.
dc.relationElham Mohammadi, Hessam Amini, and Leila Kosseim. Quick and (maybe not so) easy detection of anorexia in social media posts. In CLEF, 2019.
dc.relationAndrew Yates, Arman Cohan, and Nazli Goharian. Depression and self-harm risk assessment in online forums. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2968–2978, Copenhagen, Denmark, September 2017. Association for Computational Linguistics.
dc.relationStephen Grossberg. Recurrent neural networks. Scholarpedia, 8:1888, 01 2013.
dc.relationTomas Mikolov, Martin Karafiát, Lukás Burget, Jan Honza Cernocký, and Sanjeev Khudanpur. Recurrent neural network based language model. In INTERSPEECH, 2010.
dc.relationFarig Sadeque, Dongfang Xu, and Steven Bethard. UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection. CEUR workshop proceedings, 1866, sep 2017.
dc.relationSepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9:1735–80, 12 1997.
dc.relationAlex Sherstinsky. Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena, 404:132306, 2020.
dc.relationNing Liu, Zheng Zhou, Xin Kang, and Fuji Ren. TUAI at eRisk 2018. CEUR Workshop Proceedings, 2125, 2018.
dc.relationMarcel Trotzek, Sven Koitka, and C. Friedrich. Linguistic metadata augmented classifiers at the clef 2017 task for early detection of depression. In CLEF, 2017.
dc.relationFarig Sadeque, Dongfang Xu, and Steven Bethard. Measuring the latency of depression detection in social media. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM ’18, page 495–503, New York, NY, USA, 2018. Association for Computing Machinery.
dc.relationRazan Masood, Faneva Ramiandrisoa, and Ahmet Aker. Ude at erisk 2019: Early risk prediction on the internet. 2019.
dc.relationDavid E. Losada, Fabio Crestani, and Javier Parapar. Overview of eRisk 2018: Early Risk Prediction on the Internet (extended lab overview). CEUR Workshop Proceedings, 2125, 2018.
dc.relationDavid E. Losada, Fabio Crestani, and Javier Parapar. Overview of erisk 2019 early risk prediction on the internet. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11696 LNCS:340–357, 9 2019.
dc.relationIdriss Abdou Malam, Mohamed Arziki, Mohammed Nezar Bellazrak, Farah Benamara, Assafa El Kaidi, Bouchra Es-Saghir, Zhaolong He, Mouad Housni, Véronique Moriceau, Josiane Mothe, and Faneva Ramiandrisoa. IRIT at e-Risk. In International Conference of the CLEF Association, CLEF 2017 Labs Working Notes (CLEF 2017), volume 1866, pages pp. 1–7, Dublin, Ireland, September 2017.
dc.relationMaria Paula Villegas, Darío Gustavo Funez, Maria José Garciarena Ucelay, Leticia Cecilia Cagnina, and Marcelo Luis Errecalde. Lidic - unsl’s participation at erisk 2017: Pilot task on early detection of depression. In CLEF, 2017.
dc.relationHayda Almeida, Antoine Briand, and Marie-Jean Meurs. Detecting early risk of depression from social media user-generated content. In CLEF, 2017.
dc.relationIRIT at e-Risk 2018, Clpsych 2016 Dataset., and Annie goleman, daniel; boyatzis, Richard; Mckee. IRIT at e-Risk 2018. Journal of Chemical Information and Modeling, 53(9):1689–1699, 2019.
dc.relationAlina Trifan. BioInfo@UAVR at eRisk 2019: delving into social media texts for the early detection of mental and food disorders.
dc.relationWaleed Ragheb, Jérôme Azé, Sandra Bringay, and Maximilien Servajean. Language modeling in temporal mood variation models for early risk detection on the internet. In Fabio Crestani, Martin Braschler, Jacques Savoy, Andreas Rauber, Henning Müller, David E. Losada, Gundula Heinatz Bürki, Linda Cappellato, and Nicola Ferro, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction, pages 248–259, Cham, 2019. Springer International Publishing.
dc.relationSergio G. Burdisso, Marcelo Luis Errecalde, and Manuel Montes y Gómez. Unsl at erisk 2019: a unified approach for anorexia, self-harm and depression detection in social media. In CLEF, 2019.
dc.relationRosa María Ortega-Mendoza, D. I. H. Farías, and Manuel Montes y Gómez. Ltl-inaoe’s participation at erisk 2019: Detecting anorexia in social media through shared personal information. In CLEF, 2019.
dc.relationRon Kohavi. Glossary of terms special issue on applications of machine learning and the knowledge discovery process, 1998.
dc.relationJaved A. Aslam, Matthew Ekstrand-Abueg, Virgil Pavlu, Fernando D. Diaz, Richard McCreadie, and Tetsuya Sakai. Trec 2014 temporal summarization track overview. In TREC, 2014.
dc.relationJuan S. Lara and Fabio A. González. Dissimilarity Mixture Autoencoder for Deep Clustering. jun 2020.
dc.relationAnjana Gosain and Saanchi Sardana. Handling class imbalance problem using oversampling techniques: A review. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017-January:79–85, 11 2017.
dc.relationDiederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015.
dc.relationNisha V M and Dr. Ashok Kumar R. Implementation on text classification using bag of words model. SSRN Electronic Journal, 5 2019.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados al autor, 2021
dc.titleModelo de aprendizaje automático para la clasificación temprana de flujos de texto aplicado a la detección de desórdenes psicológicos
dc.typeTrabajo de grado - Maestría


Este ítem pertenece a la siguiente institución