Dissertation/Thesis Abstract

Needle in the Noise: Detecting Public Safety Events Over Twitter
by Paulson, Keith, M.Eng., University of Colorado Colorado Springs, 2019, 52; 13864461
Abstract (Summary)

The recent advent of using social media and search engine queries to detect and classifying events is an emerging area in data science. This study uses current Natural Language Processing (NLP) techniques for deep learning combined with classical techniques (heuristic) to detect incidents. Individual tweets will be processed into features and correlated to detect public safety related events. These features will be derived using both classic and modern techniques. Classic features include the use of sentiment/emotion detection, word encoding, named entity recognition, and other classic semantic tools. Hybrid methods will use feature hashing to reduce features for word encoding and named entities. Modern features will use word vectors (FastText). Improved activation functions (Leaky RELU), dropout, and normalization will be used to improve performance of neural networks. Classic and Modern feature and various techinques will be assessed in terms of improving performance. Various models will be used and compared to include traditional machine learning models (Support Vector Machine, Naive Bayes) as well as deep neural networks with the goal to select models that improve performance. Classical techniques will leverage the following tools: Linguistic Inquiry and Word Count (LIWC), Valence Aware Dictionary and sEntiment Reasoner (VADER), and the National Research Council Canada's (NRC) Sentiment and Emotion Lexicons, and DBPedia Spotlight Named Entity Recognition. Detected incidents will further be classified into specific types (fire, shooting, car accident). The goal is to create a combined expert system capable of detecting incidents that impact public safety more efficiently and effectively than previous techniques and will perform better than state of the art text classification tools.

Indexing (document details)
Advisor: Kalita, Jugal
Commitee: Feliciano, Leilani, Semwal, Sudhanshu
School: University of Colorado Colorado Springs
Department: Engineering and Applied Science
School Location: United States -- Colorado
Source: MAI 58/05M(E), Masters Abstracts International
Subjects: Web Studies, Artificial intelligence, Computer science
Keywords: Incident detection, Machine learning, Natural language processing, Neural network, Sentiment detection, Social media, Twitter
Publication Number: 13864461
ISBN: 978-1-392-15263-8
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