• DocumentCode
    3082013
  • Title

    Topical Evolution and Regional Affinity of Tweets

  • Author

    Dey, Lipika ; Khurdiya, Arpit ; Mahajan, Dhruv

  • Author_Institution
    Innovation Labs., Delhi Tata Consultancy Services, Gurgaon, India
  • fYear
    2013
  • fDate
    24-26 Aug. 2013
  • Firstpage
    297
  • Lastpage
    300
  • Abstract
    Business organizations are increasingly showing interest in Twitter content to know their consumers. Tracking popular tags and trends give some idea about what people are talking about. However, in order to act on the knowledge acquired, they need more detailed information like regional variability in content, exact location of discontent if any, regional affinities and influences etc. In this work, we present methods to identify topics of discussion in tweets using a LDA-based approach, which can identify emerging or evolving topics. Regional analysis of topics can provide interesting business insights about consumer expectation or behavioural variations. Further, regional distribution of topics are analysed to identify clusters of regions that tend to behave similarly over extended periods of time.
  • Keywords
    business data processing; consumer behaviour; data mining; social networking (online); statistical analysis; text analysis; LDA-based approach; Twitter content; behavioural variation; business insight; business organization; consumer expectation; popular tags; regional affinity; regional variability; topic regional distribution; topical evolution; trends; tweets; Data mining; Market research; Matrix decomposition; Media; Organizational aspects; Twitter; Clustering; Regional Dispersion; Social Media Analytics; Topic extraction; regional clusters;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Business Intelligence (ISCBI), 2013 International Symposium on
  • Conference_Location
    New Delhi
  • Print_ISBN
    978-0-7695-5066-4
  • Type

    conf

  • DOI
    10.1109/ISCBI.2013.66
  • Filename
    6724371