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portfolio
COVID-19 Spanish Sentiment Polarity Analyzer (2025)
Multimodal model (BETO + Vision Transformer) for polarity analysis of Spanish social-media posts about COVID-19, reaching a 96% F1 score.
Mexican Boxing Sentiment Polarity Analyzer (2025)
Multimodal model (BETO + Vision Transformer) analyzing Spanish social-media sentiment around Saúl «Canelo» Álvarez’s fights, reaching a 77.76% F1 score.
Text Detection in Images (2024)
A YOLOv8 / Ultralytics model to detect text embedded in images, such as memes in social-media posts.
Phishing Detection on Emails (2023)
New phishing-detection models for email, including a multilabel model that detects persuasion principles in English with LLMs.
#8M on Twitter (2023)
Social-network analysis of the 2023 International Women’s Day movement on Twitter: community detection, temporal analysis, sentiment, and user classification.
Human Rights Recommendations Labeling Automation (2023)
Multilabel models to automatically classify human-rights recommendations from intergovernmental organizations using LLMs.
Opinion Mining for Electoral Preference Modeling (2023)
Opinion mining on Twitter (now X) via the Twitter API to model electoral preferences.
Proyecto Imagen de México (2022–2023)
Analyzing Mexico’s image abroad from news and social-media sources via APIs; fine-tuned LLMs improved F1 by ~14% over previous work.
Impact of Mining in Latin America (2022)
Collection and analysis of mining-activity reports from alternative digital sources across Latin America (2010–2022).
publications
Análisis preliminar del sentimiento sobre la vacunación del COVID-19 en México
Published in Research in Computing Science, 2021
Preliminary sentiment analysis of COVID-19 vaccination in Mexico.
Recommended citation: Zúñiga-Morales, L. N., Zúñiga-López, A., Villegas-Cortez, J., Avilés-Cruz, C., & Morales-Torres, F. (2021). "Análisis preliminar del sentimiento sobre la vacunación del COVID-19 en México." Research in Computing Science, 150(5), 281–294.
Impact Evaluation of Multimodal Information on Sentiment Analysis
Published in Advances in Computational Intelligence (Springer Nature Switzerland), 2022
Evaluating the contribution of multimodal information to sentiment analysis.
Recommended citation: Zúñiga-Morales, L. N., González-Ordiano, J. Á., Quiroz-Ibarra, J. E., & Simske, S. J. (2022). "Impact Evaluation of Multimodal Information on Sentiment Analysis." In Advances in Computational Intelligence (pp. 18–29). Cham: Springer Nature Switzerland.
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Machine learning framework for country image analysis
Published in Journal of Computational Social Science, 2024
A machine-learning framework to analyze the image of a country from digital sources.
Recommended citation: Zúñiga-Morales, L. N., González-Ordiano, J. Á., Quiroz-Ibarra, J. E., & Villanueva Rivas, C. (2024). "Machine learning framework for country image analysis." Journal of Computational Social Science, 7(1), 523–547.
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Uncovering phishing attacks using principles of persuasion analysis
Published in Journal of Network and Computer Applications, 2024
Detecting phishing attacks through the analysis of persuasion principles.
Recommended citation: Bustio-Martínez, L., et al. (2024). "Uncovering phishing attacks using principles of persuasion analysis." Journal of Network and Computer Applications, 103964.
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Towards Automatic Principles of Persuasion Detection Using a Machine Learning Approach
Published in Progress in Artificial Intelligence and Pattern Recognition (Springer Nature Switzerland), 2024
A machine-learning approach to automatically detect principles of persuasion in text.
Recommended citation: Bustio-Martínez, L., et al. (2024). "Towards Automatic Principles of Persuasion Detection Using a Machine Learning Approach." In Progress in Artificial Intelligence and Pattern Recognition (pp. 155–166). Cham: Springer Nature Switzerland.
Análisis de emociones en torno a las comunidades de activistas en el contexto de las movilizaciones del #8M2023 en México
Published in Revista Iberoamericana de Comunicación, 2025
Emotion analysis of activist communities during the #8M2023 mobilizations in Mexico.
Recommended citation: Portillo Sánchez, M., González Ordiano, J. Á., Quiroz Ibarra, J. E., & Zúñiga Morales, L. N. (2025). "Análisis de emociones en torno a las comunidades de activistas en el contexto de las movilizaciones del #8M2023 en México." Revista Iberoamericana de Comunicación, 3(45), 11–34.
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Enhanced phishing detection using multimodal data
Published in Knowledge-Based Systems, 2026
A multimodal approach to improve phishing detection.
Recommended citation: Bustio-Martínez, L., et al. (2026). "Enhanced phishing detection using multimodal data." Knowledge-Based Systems, 334, 115105.
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On the impact of image and text data on multimodal sentiment analysis in Spanish
Published in Multimedia Tools and Applications, 2026
Study of how fusing text and image representations from large language models affects sentiment prediction for Spanish social-media posts.
Recommended citation: Zúñiga-Morales, L. N., González-Ordiano, J. Á., Quiroz-Ibarra, J. E., & Simske, S. J. (2026). "On the impact of image and text data on multimodal sentiment analysis in Spanish." Multimedia Tools and Applications, 85(4), 281.
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talks
teaching
Temas Selectos de Sistemas de Información
Undergraduate elective · B.S. in Actuarial Science, Universidad Iberoamericana Ciudad de México, 2026
Elective course bridging operations research and deep learning: it covers decision-making with mathematical, statistical, and probabilistic tools, linear optimization, and an introduction to deep neural networks and their current applications. Group A · 6-week intensive term (May 25 – July 3, 2026) · 60 total hours. Prerequisites: linear algebra, multivariable calculus, and structured programming (Python). Feel free to ask for the slides of the lessons.
Inteligencia de Datos
Undergraduate course · B.S. in Actuarial Science (8th semester), Universidad Iberoamericana Ciudad de México, 2026
Advanced data-science course analyzing the trends and paradigms of data science — its mathematical foundations and the development of models — to understand and carry out the full life cycle of a data-science project. Group A · Fall 2026 (17 weeks · 68 hours). Prerequisites: multivariable calculus, nonlinear optimization, linear algebra, probability, and structured programming (Python). Feel free to ask for the slides of the lessons.
Machine Learning
Undergraduate course · B.S. in Actuarial Science (7th semester), Universidad Iberoamericana Ciudad de México, 2026
Course on the trends and paradigms of machine learning, covering the mathematical foundations and the development of different models for their implementation in data-science projects. Group A1 · Fall 2026 (17 weeks · 34 classes · 68 hours). Prerequisites: multivariable calculus, nonlinear optimization, linear algebra, probability, and structured programming (Python). Feel free to ask for the slides of the lessons.