Use of social media and Natural Language Processing (NLP) in natural hazard research
This work addresses the challenge of real-time natural hazard detection for researchers and emergency responders, but it is incremental as it builds on prior studies by applying existing NLP tools to this domain.
The authors tackled the problem of detecting natural hazards like earthquakes and typhoons by processing and classifying text from Twitter tweets using natural language processing (NLP) and machine learning with TensorFlow, resulting in a method for event classification based on text files.
Twitter is a microblogging service for sending short, public text messages (tweets) that has recently received more attention in scientific comunity. In the works of Sasaki et al. (2010) and Earle et al., (2011) the authors explored the real-time interaction on Twitter for detecting natural hazards (e.g., earthquakes, typhoons) baed on users' tweets. An inherent challenge for such an application is the natural language processing (NLP), which basically consists in converting the words in number (vectors and tensors) in order to (mathematically/ computationally) make predictions and classifications. Recently advanced computational tools have been made available for dealing with text computationally. In this report we implement a NLP machine learning with TensorFlow, an end-to-end open source plataform for machine learning applications, to process and classify evenct based on files containing only text.