Ciencias Exactas y Ciencias de la Salud

Now showing 1 - 2 of 2

The role of capitalization and character repetition in identifying depression on social Media: a bilingual approach
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-24) Burgueño Paz, Luis Humberto; Zareei, Mahdi; emipsanchez; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Monterrey; García Ceja, Enrique Alejandro
Depression is a mental disorder that affects millions of people worldwide, but a significant portion of the affected people don’t receive adequate treatment. There has been an increasing interest from researchers to detect this condition through social media posts in order to prompt for early treatment. However, most of the research has been focused on the Caucasian Western English-speaking population, limiting the applicability of their findings across diverse cultural contexts. While research has shown the use of nonverbal cues to convey sentiment, their role on depression detection remains under-explored. This thesis aims to assess the effect of nonverbal cues, specifically capitalization and character repetition, on depression detection using datasets both in English and Spanish. This effect was explored through three existing datasets. The first dataset included a collection of Reddit posts and comments in the English language and was selected to assess the effect on a dataset coming from one of the most reputable mental health competitions in Natural Language Processing. The second dataset consisted of a collection of Spanish- language messages from Telegram to verify whether findings in the English language would hold for Spanish. The third dataset, also built from Reddit posts, was used to analyze the impact of these features when classifying by depression severity levels rather than binary labels. Four classifiers were used throughout this research: Logistic Regression, Random Forest, Support Vector Machine, and Neural Network. Overall, the impact of capitalization and character repetition for depression detection was found to be minimal. These features had the most effect on English Reddit data with binary labels, while showing limited impact on Spanish data or when classifying by severity levels. Additionally, models using only character repetition outperformed those relying on capitalization features.
One step closer to mental health: resilience to mental stress index in the face of analytical problems, a machine learning approach
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-01) Díaz Ramos, Ramón Eduardo; Trejo Rodríguez, Luis Angel; puemcuervo; Medina Pérez, Miguel Angel; González Mendoza, Miguel; Figueroa López, Carlos Gonzalo; School of Engineering and Sciences; Campus Monterrey
Stress and depression are two major topics of concern for higher education institutions. Studies have shown how mental health problems can decrease students' ability to function efficiently during their education life and how depression can risk their physical well-being. To aid students in coping with the challenging experience of higher education and therefore enable them to perform better in stressful situations post-graduation, researchers recommend increasing their level of resilience. In an attempt to measure a person's resilience, previous studies have developed and analyzed self-rating questionnaires. While these studies have provided a way to assess people's psychological responses and provided a significant amount of insight, they do not provide an objective measurement for resilience to mental stress. There have been related studies that have evaluated physiological signals in individuals and have identified relationships with people's stress. Based on previous literature and applying machine learning, this thesis aims to demonstrate the feasibility of measuring an individual's resilience to mental stress and proposes a Resilience to Mental Stress Index (RMSI). In addition to this, this thesis presents an experiment's methodology to collect physiological and psychological data using smartwatch embedded sensors and psychological tools to study depression prediction. This research performed data analysis of 71 individuals subjected to a 10-minute psychophysiological test to study resilience to mental stress. The data collected considers five physiological features: (a) muscle response (electromyography), (b) blood volume pulse, (c) breathing rate, (d) peripheral temperature, and (e)skin conductance. We utilized unsupervised learning techniques to visualize and identify the relationship between these feature variability. As a result of the analysis, we created three different methods for the RMSI. The results' analysis between the different methods showed no statistically significant difference (p>0.05). However, we recommend using the Mahalanobis distance (MD) method because of its relationship with the validation methods. Even though there exists no standard method to quantify resilience to mental stress, our results indicate a positive relationship to the Resilience in Mexicans (RESI-M) psychological tool. For the study of depression prediction, during this research, five variables were selected for the study: (a) personality traits, (b) RMSI, (c) heart rate variability (HRV), and (d) sleeping disorders. To collect these variables, we developed a methodological framework and built a mobile application. We hope that this research serves as a solid baseline to understand resilience to mental stress and collect valuable information to predict depression.

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results