Aprendizaje automático aplicado al análisis de sentimientos

With the evolution of the Internet, there is a large amount of information present on the web such as the opinions of users or consumers about different contexts, either to express their agreement or disagreement about a product or service received, as well as the opinion of a item purchased or about the management performed by someone. Due to the large number of opinions, comments and suggestions from users, it is very important to explore, analyze and organize their views to make better decisions. Sentiment analysis is a natural language processing and information extraction task that identifies the opinions of the users explained in the form of positive, negative or neutral comments. Several techniques can be used for this purpose, for example the use of lexical dictionaries that has been widely used and recently the use of artificial intelligence specifically supervised algorithms. In this document, we propose the use of supervised algorithm techniques to observe their use and see the performance of different models of supervised algorithms to measure the effectiveness in the classification of a data set.Con la evolución del Internet, hay una gran cantidad de información presente en la web como lo son las opiniones de los usuarios o  consumidores sobre diversos contextos ya sea para expresar su conformidad o inconformidad sobre un producto o servicio recibido, así como la opinión de un artículo comprado o sobre la gestión que realiza alguna persona. Debido a la gran cantidad de opiniones, comentarios y sugerencias de los usuarios, es muy importante explorar, analizar y organizar sus puntos de vista para tomar mejores decisiones. El análisis de sentimientos es una tarea de procesamiento de lenguaje natural y extracción de información que identifica las opiniones de los usuarios explicadas en forma de comentarios positivos, negativos o neutrales. Varias técnicas pueden ser utilizadas para este fin, por ejemplo el uso de diccionarios léxicos que ha sido muy utilizada y recientemente la utilización de la inteligencia artificial específicamente algoritmos supervisados. En este documento, se propone la utilización de técnicas de algoritmos supervisados para observar su utilización y ver el rendimiento de diferentes modelos de algoritmos supervisados para medir la efectividad en la clasificación de un conjunto de datos

Arquitectura de PLN aplicada al contexto de la salud mental

At a global level, the situation caused by COVID-19 has created a worrying and discouraging reality, especially for governments, especially for the most vulnerable populations due to the fact that they do not know how to eradicate the pandemic, many have not been able to overcome the challenges mainly emerging from an infectious disease with implications for physical health and which has also profoundly affected people's mental health and well-being. Mental health affectations are problems that affect all of us at some point in our lives, either due to experiences we have experienced or even biological factors. Paying attention and providing the necessary support at an early stage is the key to preventing more severe effects. The discipline of natural language processing (NLP) is a sub-area of artificial intelligence (AI) that studies the interactions between computers and the language that humans speak. This research proposes the design and implementation of a comprehensive architecture based on AI, machine learning (ML) and PLN components, which will allow us to detect and analyze behavior patterns in people and generate possible early diagnoses of mental health diseases.A nivel global la situación acarreada por COVID-19, ha creado una realidad preocupante y desalentadora especialmente a los gobiernos en especialmente a las poblaciones más vulnerables por el hecho de desconocer como erradicar la pandemia, muchos no han podido superar los desafíos principalmente emergentes de una enfermedad infecciosa con implicaciones para la salud física y que también ha afectado profundamente la salud mental y el bienestar de las personas. Las afectaciones de salud mental son problemas que nos afectan a todos en algún momento de nuestras vidas, ya sea por experiencias que hemos vivido o incluso factores biológicos. Prestarle la atención y brindar el apoyo necesario en una etapa temprana es la clave para prevenir afectaciones más severas. La disciplina del procesamiento de lenguaje natural (PLN), es una sub área inteligencia artificial (IA) que estudia las interacciones entre las computadoras y el lenguaje que hablamos los humanos. En esta investigación se propone el diseño e implementación de una arquitectura integral basada en componentes de IA, aprendizaje automático (ML) y PLN, la cual nos permitirá detectar y analizar patrones de comportamiento en las personas y generar posibles diagnósticos tempranos a enfermedades de salud mental

Mobile Recommendation System to Provide Emotional Support and Promote Active Aging for Older Adults in the Republic of Panama

Aging brings with it physical and cognitive changes that can lead to health problems such as chronic disease and cognitive impairment. Technology is a fundamental ally in improving the quality of life of older adults by enabling accurate and early diagnosis. In this context, we present a mobile application designed to provide emotional support and guidance, thus contributing to the well-being of this demographic group. Our study was based on quantitative research methods, using an experimental approach on a sample of users aged between 60 and 80 years. The results showed that 93.3% of users found the app to be a useful resource for adopting a healthier lifestyle. The app provides specific recommendations, such as breathing exercises to reduce anxiety, recreational activities, exercises tailored to physical ability, and meditation practices. These specific features have been shown to improve the well-being of older adults by providing a personalized approach to the challenges of aging

Mobile Applications for Diabetes Self-Care and Approach to Machine Learning

Diabetes is a silent disease, the number of people who suffer from it increases daily, it is unfortunate that many young people develop this condition and do not know that they suffer from it. So much so that this disease is the fifth cause of death in Panama. Using software technologies applied to areas such as health every day is increasing. Scientific research in health areas, as well as the development of new technologies that involve smartphones and sensors, is making health self-care possible. Currently, interest in mobile health (mHealth) applications for disease self-care is growing. The innovation of technological tools associated with artificial intelligence is increasing every day. Among its most radical trends is machine learning, whose function is to develop techniques that allow computers to learn. This learning occurs through the data that can be provided to the algorithms responsible for categorizing. Therefore, this research aims to analyze mobile applications specifically those focused on diabetes, to propose an emerging systematic model of medical care for self-management of patients with diabetes and, finally, achieve a reliable data set with Panamanian patient data to apply machine-learning models and see how much we can help Panamanian doctors

Mobile Applications for Diabetes Self-Care and Approach to Machine Learning

Diabetes is a silent disease, the number of people who suffer from it increases daily, it is unfortunate that many young people develop this condition and do not know that they suffer from it. So much so that this disease is the fifth cause of death in Panama. Using software technologies applied to areas such as health every day is increasing. Scientific research in health areas, as well as the development of new technologies that involve smartphones and sensors, is making health self-care possible. Currently, interest in mobile health (mHealth) applications for disease self-care is growing. The innovation of technological tools associated with artificial intelligence is increasing every day. Among its most radical trends is machine learning, whose function is to develop techniques that allow computers to learn. This learning occurs through the data that can be provided to the algorithms responsible for categorizing. Therefore, this research aims to analyze mobile applications specifically those focused on diabetes, to propose an emerging systematic model of medical care for self-management of patients with diabetes and, finally, achieve a reliable data set with Panamanian patient data to apply machine-learning models and see how much we can help Panamanian doctors. </p