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.
|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|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be