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Analysis and Visualization of Sentiment and Emotion on Crisis Tweets

  • Megan K. Torkildson
  • Kate Starbird
  • Cecilia Aragon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8683)

Abstract

Understanding how people communicate during disasters is important for creating systems to support this communication. Twitter is commonly used to broadcast information and to organize support during times of need. During the 2010 Gulf Oil Spill, Twitter was utilized for spreading information, sharing firsthand observations, and to voice concern about the situation. Through building a series of classifiers to detect emotion and sentiment, the distribution of emotion during the Gulf Oil Spill can be analyzed and its propagation compared against released information and corresponding events. We contribute a series of emotion classifiers and a prototype collaborative visualization of the results and discuss their implications.

Keywords

Sentiment Analysis Twitter Machine Learning 

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References

  1. 1.
    Brooks, M., Kuksenok, K., Torkildson, M., Perry, D., Robinson, J., Scott, T., Anicello, O., Zukowski, O., Harris, P., Aragon, C.: Statistical Affect Detection in Collaborative Chat. In: Proceedings of CSCW 2013, pp. 317–328 (2013)Google Scholar
  2. 2.
    Ekman, P.: An argument for basic emotions. Cognition & Emotion 6(3-4), 169–200 (1992)CrossRefGoogle Scholar
  3. 3.
    Roberts, K., Roach, M., Johnson, J., Guthrie, J., Harabagiu, S.: Empatweet: Annotating and detecting emotions on Twitter. In: Proceedings of the LREC, pp. 3806–3813 (2012)Google Scholar
  4. 4.
    Schulz, A., Thanh, T., Paulheim, H., Schweizer, I.: A Fine-Grained Sentiment Analysis Approach for Detecting Crisis Related Microposts. In: Proceedings of the 10th International ISCRAM Conference, pp. 846–851 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Megan K. Torkildson
    • 1
  • Kate Starbird
    • 1
  • Cecilia Aragon
    • 1
  1. 1.University of WashingtonSeattleUSA

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