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Extracting social events based on timeline and sentiment analysis in twitter corpus

  • Bayar Tsolmon
  • , A. Rong Kwon
  • , Kyung Soon Lee*
  • *Corresponding author for this work
    • Jeonbuk National University

    Research output: Contribution to conferenceConference paperpeer-review

    Abstract

    We propose a novel method for extracting social events based on timeline and sentiment analysis from social streams such as Twitter. When a big social issue or event occurs, it tends to dramatically increase in the number of tweets. Users write tweets to express their opinions. Our method uses these timeline and sentiment properties of social media streams to extract social events. On timelines term significance is calculated based on Chi-square measure. Evaluating the method on Korean tweet collection for 30 events, our method achieved 94.3% in average precision in the top 10 extracted events. The result indicates that our method is effective for social event extraction.

    Original languageEnglish
    Title of host publicationNatural Language Processing and Information Systems - 17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012, Proceedings
    Pages265-270
    Number of pages6
    DOIs
    StatePublished - 2012
    Event17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012 - Groningen, Netherlands
    Duration: 2012.06.262012.06.28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7337 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012
    Country/TerritoryNetherlands
    CityGroningen
    Period12.06.2612.06.28

    Keywords

    • Chi-Square
    • Event extraction
    • Sentiment
    • Timeline
    • Twitter

    Quacquarelli Symonds(QS) Subject Topics

    • Computer Science & Information Systems

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