Mining twitter features for event summarization and rating

Conference paper (2017)

Authors

Deepa Mallela Boise State University

Dirk Ahlers Norwegian University of Science and Technology (NTNU)

M.S. Pera Boise State University

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External organisation

Twitter Event Summarization Spatio-Temporal analysis

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Published Date

23-08-2017

Language

English

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Abstract

We present CEST, a generic method for detection and rich summarization of events occurring in a city. CEST exploits Twitter metadata, does not need prior information on events, and is event category and structure agnostic. We developed CEST to process unstructured documents and take advantage of shorthand notations, hashtags, keywords, geographical and temporal data, as well as sentiment within tweets to both detect and summarize arbitrary events without prior knowledge. We also introduce a novel strategy that analyzes sentiment and tweeting behavior over time to create a qualitative score that captures events' overall appeal to attendees.