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Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment

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Published:05 May 2012Publication History

ABSTRACT

In this paper we describe the preliminary results and future directions of a research in progress, which aims at assessing the hashtag effectiveness as a resource for sentiment analysis expressed on Twitter. The results so far support our hypothesis that hashtags may facilitate the detection and automatic tracking of online population sentiment about different events.

References

  1. C. Chew and G. Eysenbach. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS ONE, 5(11):e14118+, 2010.Google ScholarGoogle Scholar
  2. E. Cunha, G. Magno, G. Comarela, V. Almeida, M. A. Goncalves, and F. Benevenuto. Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In Proc. of the Workshop on LSM, pages 58--65, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Diakopoulos and M. Naaman. Topicality, time, and sentiment in online news comments. In Proc. EA CHI, pages 1405--1410, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. N. A. Diakopoulos and D. A. Shamma. Characterizing debate performance via aggregated twitter sentiment. In Proc. CHI, pages 1195--1198, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Gomide, A. Veloso, W. M. Jr., V. Almeida, F. Benevenuto, F. Ferraz, and M. Teixeira. Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In ACM WebSci, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Micro-blogging as online word of mouth branding. In EA CHI, pages 3859--3864, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Miller, C. Sathi, D. Wiesenthal, J. Leskovec, and C. Potts. Sentiment Flow Through Hyperlink Networks. In Proc. AAAI CWSM, 2011.Google ScholarGoogle Scholar
  9. B. Pang and L. Lee. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2:1--135, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. M. Romero, B. Meeder, and J. Kleinberg. Dierences in the mechanics of information diusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proc. WWW, pages 695--704, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. W. Willinger, R. Rejaie, M. Torkjazi, M. Valafar, and M. Maggioni. Research on online social networks: time to face the real challenges. SIGMETRICS Perform. Eval. Rev., pages 49--54, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment

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