Daniela principal research interests is related to Computer Vision, Pattern Recognition, Intelligent Video Surveillance and Text classification.
INGEOTEC research interest is text categorization seen as a supervised learning problem, that is, as a classification task. In this problem, we have developed two text modeling techniques that represent the text in a vector space model and use a Support Vector Machine as a classifier. These techniques are B4MSA which is a sentiment analysis classifier and microTC a general text classifier. In addtion this, we have been working on novel classifiers based on Genetic Programming EvoDAG.
In sentiment analysis, we have participated in a number of sentiment analysis competitions such as:
César Pérez Fernández
Tesis: Reconocimiento facial con Kinect Grado: Ingeniería Técnica en Informática de Sistemas Universidad Rey Juan Carlos, España, 2013.
Fernando Flores García
Tesis: Influencia de la aplicación de filtros de Gabor para la detección de personas Grado: Ingeniería Informática Universidad Rey Juan Carlos, España, 2013.
B4MSA is a Python Sentiment Analysis Classifier for Twitter-like short texts. It can be used to create a first approximation to a sentiment classifier on any given language. It is almost language-independent, but it can take advantage of the particularities of a language.
microTC follows a minimalistic approach to text classification.
It is designed to tackle text-classification problems in an agnostic way,
being both domain and language independent.
Currently, we only produce single-label classifiers; but support for multi-labeled problems is in the roadmap.
117 Circuito Tecnopolo Norte Col. Tecnopolo Pocitos II, C.P. 20313, Aguascalientes, Ags, México. Tel. +52 (449) 994 51 50 Ext. 5230
email: dmoctezuma at centrogeo.edu.mx