2017
EMNLP
EMNLP 2017
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters
Abstract
AbstractWe study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters. We will release the data and source code after this work is published.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning and Natural Language Processing
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Trend Setter
— Social Media Analysis
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Keyword Pioneer
— natural disaster
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Hot Topic Early Bird
— probabilistic modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Sentiment Analysis
Natural Language Processing > Applications > Information Extraction
Natural Language Processing > Applications > Sentiment Analysis
Machine Learning > Core Methods > Topic Modeling
Data Science & Analytics > Applications > Social Media Analysis
Machine Learning > Learning Types > Topic Modeling