Journal ArticleOpen Access
Theme-Driven Keyphrase Extraction to Analyze Social Media Discourse
Authors
Author Affiliations
Dartmouth College, Bangladesh University of Engineering and Technology
Published InProceedings of the International AAAI Conference on Web and Social Media
Year2024
Citations9
Abstract
Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to identify salient concepts in social media discourse without being constrained by predefined entity classes. This paper introduces a theme-driven keyphrase extraction framework tailored for social media, a pioneering approach designed to capture clinically relevant keyphrases from user-generated health texts. Themes are defined as broad categories determined by the objectives of the extraction task. We formulate this novel task of theme-driven keyphrase extraction and demonstrate its potential for efficiently mining social media text for the…
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