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.

翻译：Twitter是一种用于发送简短公开文本信息（推文）的微博客服务，近期在科学界受到更多关注。在Sasaki等人（2010年）和Earle等人（2011年）的研究中，作者们探索了基于用户推文在Twitter上的实时交互来检测自然灾害（如地震、台风）的方法。此类应用的内在挑战在于自然语言处理，其核心是将词汇转换为数值形式（向量和张量），以便从数学/计算角度进行预测和分类。近年来，先进的文本计算工具已可供使用。本报告利用TensorFlow（一个端到端的开源机器学习应用平台）实现基于自然语言处理的机器学习，对仅包含文本的文件所描述的事件进行处理和分类。