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.

Browse

Filters

20211

Settings

¡Quiero depositar mi trabajo!
Author: search.filters.author.Miranda Peña, Ana Clarissa

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Tesis de maestría
    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, Laura
    Beyond 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.
En caso de no especificar algo distinto, estos materiales son compartidos bajo los siguientes términos: Atribución-No comercial-No derivadas CC BY-NC-ND http://www.creativecommons.mx/#licencias
logo

El usuario tiene la obligación de utilizar los servicios y contenidos proporcionados por la Universidad, en particular, los impresos y recursos electrónicos, de conformidad con la legislación vigente y los principios de buena fe y en general usos aceptados, sin contravenir con su realización el orden público, especialmente, en el caso en que, para el adecuado desempeño de su actividad, necesita reproducir, distribuir, comunicar y/o poner a disposición, fragmentos de obras impresas o susceptibles de estar en formato analógico o digital, ya sea en soporte papel o electrónico. Ley 23/2006, de 7 de julio, por la que se modifica el texto revisado de la Ley de Propiedad Intelectual, aprobado

DSpace software copyright © 2002-2025

Licencia