Dr Felipe Bravo Márquez
Qualifications: PhD Waikato, MSc Uchile, BEng Uchile (Industrial Engineering), BEng Uchile (Computer Science)
I received my PhD degree from the University of Waikato. Previously, I received two engineering degrees in the fields of computer science and industrial engineering, and a masters degree in computer science, all from the University of Chile. I worked for three years as a research engineer at Yahoo! Labs Latin America.
My research interests and expertise lie in the acquisition of knowledge and information from unstructured data, particularly natural language text, spanning the overlapping fields of natural language processing (NLP), data mining (DM), machine learning (ML), information retrieval (IR), and social media analysis (SMA). My main research goals are to study society by computationally analysing traces left by humans in the digital space.
Mohammad, S., & Bravo-Marquez, F. (2017). Emotion intensities in Tweets. In Proc 6th Joint Conference on Lexical and Computational Semantics (pp. 65-77). Vancouver, Canada: Association for Computational Linguistics. doi:10.18653/v1/S17-1007
Velásquez, J. D., Covacevich, Y., Molina, F., Marrese-Taylor, E., Rodríguez, C., & Bravo Marquez, F. (2016). DOCODE 3.0 (DOcument COpy DEtector): A system for plagiarism detection by applying an information fusion process from multiple documental data sources. Information Fusion, 27, 64-75. doi:10.1016/j.inffus.2015.05.006 Open Access version: hdl:10289/9631
Bravo Marquez, F., Frank, E., Mohammad, S. M., & Pfahringer, B. (2016). Determining word–emotion associations from tweets by multi-label classification. In Proc 2016 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 536-539). Omaha, Nabraska, USA: IEEE Computer Society. doi:10.1109/WI.2016.90 Open Access version: hdl:10289/10783
Bravo Marquez, F., Frank, E., & Pfahringer, B. (2016). From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach. In Proc 2016 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 145-152). Omaha, Nebraska, USA: IEEE Computer Society. doi:10.1109/WI.2016.29 Open Access version: hdl:10289/10782