Dra. Daniela Moctezuma

Since September 2014, I work at CONACYT as a researcher and I am commissioned to CentroGEO. I am part of INGEOTEC research group.

My principal research interests are related to Computer Vision, Pattern Recognition, Intelligent Video Surveillance, Text classification and Vision & Language. For full access to my publications please visit my Scholar Google profile.

In text classification and Vision & language areas, we (INGEOTEC group) have participated in a number of competitions such as:

  • RedICA Text-Image Matching (RICATIM) Challenge. I3GO+ obtained the 1st place in the development and final phase (see Results).
  • TASS'17 (Spanish). INGEOTEC obtained the 1st place (11 teams) in Task 1 (General Corpus of TASS) (see Proceedings).
  • PAN'17 (Arabic, English, Portuguese and Spanish). INGEOTEC (Tellez et al.) obtained the 3rd place (22 participants) in global ranking (see Results)
  • SemEval'17 (English and Arabic). INGEOTEC obtained the 6th place (69 participants) in English (see Results) and 4th (18 participants) in Arabic (see Results).
  • SENTIPOLC'16 (Italian). INGEOTEC obtained 5th place (15 participants) in subjective classification and 9th (15 participants) in polarity classification (see Proceeding).
  • TASS'16 (Spanish). INGEOTEC obtained the 3rd place in 3 and 5 polarity levels (see Proceedings).
  • TASS'15 (Spanish). This is our first competition where it was obtained 12th (17 participants) in 5 polarity levels and 10th (17 participants) in 3 polarity levels (see Proceedings)).

Current Students

  • M.C. Abel Coronado (PhD Student working on remote sensing problems)
  • M.C. José Manuel Aguilera López (PhD Student working on image classification problems).

Past Students

  • 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.


A Baseline for Multilingual Sentiment Analysis (B4MSA)

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.

It is written in Python making use of NTLK, scikit-learn and gensim to create simple but effective sentiment classifiers.


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.

microTC is intentionally simple, so only a small number of features where implemented. However, it uses a some complex tools from gensimnumpy and scikit-learn.


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