Harnessing the power of hashtags in tweet analytics

Vibhuti Gupta, Rattikorn Hewett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Twitter is one of the most popular microblogging platforms where users can interact with each other by posting texts of up to 140 characters called tweets. Because of the large and fast growing number of tweets being generated daily, tweet analytics is viewed as one of the fundamental problems of Big data stream. Recently, hashtags, hyperlinked words, in tweets have been applied for tweet retrieval, trend/event detection and advertisement. However, using hashtags for tweet classification remains challenging because we have to cope with context dependent words, slangs, abbreviations, and emoticons with a limited small number of words and an evolving use of hashtags. Most existing approaches deal with classifying tweet sentiments by using the lexicon and meaning of hashtags. Our research aims to classify tweets by topics. Unlike sentiment analytics, hashtags for describing a topic need to be more diverse to cover various aspects of a topic. This paper presents a tweet analytics approach that uses domain-specific knowledge to create a set of strong hashtag predictors for tweet topic classification. The paper describes the approach and preliminary experiments that show promising results toward Big data tweet analytics.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2390-2395
Number of pages6
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Conference

Conference5th IEEE International Conference on Big Data, Big Data 2017
Country/TerritoryUnited States
CityBoston
Period12/11/1712/14/17

Keywords

  • Big Data Stream
  • Hashtags
  • Ontology
  • Social Media
  • Twitter

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