2017
EMNLP
EMNLP 2017
Topic Signatures in Political Campaign Speeches
Abstract
AbstractHighlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign. The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning and Natural Language Processing
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Trend Setter
— Topic Modeling
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Keyword Pioneer
— political speech
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Hot Topic Early Bird
— text mining
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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
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Applications > Information Retrieval
Natural Language Processing > Resources & Methods > Text Representation
Data Science & Analytics > Methods > Data Mining
Natural Language Processing > Resources & Methods > Language Modeling
Machine Learning > Core Methods > Topic Modeling
Natural Language Processing > Applications > Topic Modeling