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Tweetviz: Visualizing Tweets for Business Intelligence

Published:07 July 2016Publication History

ABSTRACT

Social media offers potential opportunities for businesses to extract business intelligence. This paper presents Tweetviz, an interactive tool to help businesses extract actionable information from a large set of noisy Twitter messages. Tweetviz visualizes the tweet sentiment of business locations, identifies other business venues that Twitter users visit, and estimates some simple demographics of the Twitter users frequenting a business. A user study to evaluate the system's ability indicates that Tweetviz can provide an overview of a business's issues and sentiment as well as information aiding users in creating customer profiles.

References

  1. F. Chen, D. Joshi, Y. Miura, and T. Ohkuma. Social media-based profiling of business locations. In Proceedings of the 3rd ACM Multimedia Workshop on Geotagging and Its Applications in Multimedia, pages 1--6. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Diakopoulos, M. Naaman, and F. Kivran-Swaine. Diamonds in the rough: Social media visual analytics for journalistic inquiry. In Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on, pages 115--122. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller. Twitinfo: aggregating and visualizing microblogs for event exploration. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 227--236. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. O'Connor, M. Krieger, and D. Ahn. Tweetmotif: Exploratory search and topic summarization for twitter. In Proceedings of the International AAAI Conference on Weblogs and Social Media, 2010.Google ScholarGoogle Scholar

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  1. Tweetviz: Visualizing Tweets for Business Intelligence

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      • Published in

        cover image ACM Conferences
        SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
        July 2016
        1296 pages
        ISBN:9781450340694
        DOI:10.1145/2911451

        Copyright © 2016 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 July 2016

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        Acceptance Rates

        SIGIR '16 Paper Acceptance Rate62of341submissions,18%Overall Acceptance Rate792of3,983submissions,20%

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