Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Analyzing fan avidity for soccer prediction(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-09) Miranda Peña, Ana Clarissa; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; emijzarate/puemcuervo; Hernandez Gress, Neil; Alvarado Uribe, Joanna; Escuela de Ingeniería en Ciencias; Campus Monterrey; Hervert Escobar, LauraBeyond being a sport, soccer has built up communities. Fans showing interest, involvement, passion and loyalty to a particular team, something known as Fan Avidity, have strengthen the sport business market. Social Networks have made incredibly easy to identify fans’commitment and expertise. Among the corpus of sport analysis, plenty of posts with a well substantiated opinion on team’s performance and reliability are wasted. Based on graph theory, social networks can be seen as a set of interconnected users with a weighted influence on its edges. Evaluating the spread influence from fans' posts retrieved from Twitter could serve as a metric for identifying fans’ intensity, if adding sentiment classification, then it is possible to score Fan Avidity. Previous work attempts to engineer new key performance indicators or apply machine learning techniques for identifying the best existing indicators, however, there is limited research on sentiment analysis. In order to achieve the Master's Degree in Computer Science, this thesis aims to strengthen a machine learning model that applies polarity and sentiment analysis on tweets, as well as discovering factors thought to be relevant on a soccer match. The final goal is to achieve a flexible mechanism which automatizes the process of gathering data before a match, with the main objective of quantifying credit on fans' sentiment along with historical factors, while evaluating soccer prediction. The left alone sentiments' model could accomplish independence from the type of tournament, league or even sport.
- Contrast pattern-based classification on sentiment features for detecting people with mental disorders on social media(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-22) Gallegos Salazar, Leslie Marjorie; LOYOLA GONZALEZ, OCTAVIO; 553351; Loyola-González, Octavio; emipsanchez; School of Engineering and Science; Campus Estado de México; Medina-Pérez, Miguel AngelMental disorders are a global problem that widely affects different segments of the population. Mental disorders present consequences in the life of those suffering from them as they can have difficulties performing daily tasks normally. However, consequences in the economy, society, human rights, and cultural scope are also present as it is a problem that has been growing for a long time. Diagnosis and treatment are difficult to obtain as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. Specialists in varied areas have proposed multiple solutions for the detection of the risk of depression; the computer science field has proposed some, based on language use and the data obtained through social media. Those solutions are mainly focused on objective features like n-grams and lexicons. We propose a contrast pattern-based classifier for detecting depression by using a new data representation based only on sentiment and emotion analysis extracted from post on social networks. The representation contains 28 different features which include information on sentiment, emotion, polarity, sarcasm, and other subjective information of the text. We then used a classifier that has not been used before in the state-of-the-art and obtained an AUC between 0.71 and 0.72. Finally we reproduced state-of-the-art models and statistically compared them with the result of the proposed model. The results show no significant statistical difference with a reproduction of the models found in the state-of-the-art. Furthermore, with the classifier used we were able to provide an explanation close to the language of an expert on the decision of the classifier.