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Dr Felipe Bravo Márquez

Research Fellow

Qualifications: PhD Waikato, MSc Uchile, BEng Uchile (Industrial Engineering), BEng Uchile (Computer Science)

Contact Details

Email: felipe.bravomarquez@waikato.ac.nz
Room: G.1.05
Phone: +64 7 837 9642
Extension: 9642
Website: http://www.cs.waikato.ac.nz/~fbravoma/

About Felipe

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.

Research Interests

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.

Recent Publications

  • Mohmmad, S. M., & Bravo-Marquez, F. (2017). WASSA-2017 shared task on emotion intensity. In Proc 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 34-49). Copenhagen, Denmark: Association for Computational Linguistics. Retrieved from http://aclweb.org/anthology/W17-5205

  • 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

  • Bravo Marquez, F., Frank, E., & Pfahringer, B. (2016). Building a Twitter opinion lexicon from automatically-annotated tweets. Knowledge-Based Systems, 108(15), 65-78. doi:10.1016/j.knosys.2016.05.018 Open Access version: hdl:10289/10754

  • 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

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