Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

Abstract

The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions' testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it.

Topic Jerk Detector: Detection of Tweet Bursts Related to the Fukushima Daiichi Nuclear Disaster

“…Some of the limitations of these methods are the expensive computational cost and vague definitions of burst strength. Many recently developed machine learning approaches are characterized by the same problems [22,23]. In an attempt to solve these problems, "topic dynamics" was proposed as an alternative model [24].…”