Authors:
Neelesh Rastogi
and
Fazel Keshtkar
Affiliation:
St. John’s University, 8000 Utopia Pkwy, Queens, New York 11439, U.S.A.
Keyword(s):
Social Network Streams, Classification, Neural Networks, BERT, Word Embedding, Topic Modelling, Emergency Response, Sentiment Analysis, Disease Ontology.
Abstract:
Predicting disease outbreaks has been a focus for various institutions and researchers for many years. However, any models that seemed to get close to resolve this issue have failed to predict potential outbreaks with accuracy over time. For leveraging the social media data effectively, it is crucial to filter out noisy information from the large volume of data flux so that we could better estimate potential disease outbreaks with growing social data. Not satisfied with essential keyword-based filtration, many researchers turn to machine learning for a solution. In this paper, we apply deep learning techniques to address the Tweets classification problem concerning disease outbreak predictions. To achieve this, we curated a labeled corpus of Tweets that reflect different types of disease-related reports, showcasing diverse community sentiment and varied potential usages in emergency responses. Further, we used BERT, a word embedding and deep learning method to apply transfer learning
against our curated dataset. Applying BERT showed that it performs better in comparable results to Long short-term memory (LSTM) and outperforming the baseline model on average in terms of accuracy and F-score.
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