Mining twitter features for event summarization and rating

Deepa Mallela, Dirk Ahlers, Maria Soledad Pera

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
PublisherAssociation for Computing Machinery (ACM)
Pages615-622
Number of pages8
ISBN (Electronic)9781450349512
DOIs
Publication statusPublished - 23 Aug 2017
Externally publishedYes
Event16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 - Leipzig, Germany
Duration: 23 Aug 201726 Aug 2017

Conference

Conference16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
Country/TerritoryGermany
CityLeipzig
Period23/08/1726/08/17

Keywords

  • Event
  • Spatio-Temporal analysis
  • Summarization
  • Twitter

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