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Sentiment Characterization of an Urban Environment via Twitter

  • Víctor Martínez
  • Víctor M. Gonzílez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)

Abstract

We propose a statistical study of sentiment produced in an urban environment by collecting tweets submitted in a certain timeframe. Each tweet was processed using our own sentiment classifier and assigned either a positive or a negative label. By calculating the average mood, we were able to run a Mann-Withney’s U test to evaluate differences in the calculated mood per day of week. We found that all days of the week had significantly different medians. We also found positive correlations between Mondays and the rest of the week.

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References

  1. 1.
    Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: ICWSM (2011)Google Scholar
  2. 2.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11, 122–129 (2010)Google Scholar
  3. 3.
    Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1195–1198. ACM (2010)Google Scholar
  4. 4.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)CrossRefGoogle Scholar
  5. 5.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Víctor Martínez
    • 1
  • Víctor M. Gonzílez
    • 1
  1. 1.Department of Computer ScienceInstituto Tecnologico Autonomo de MexicoMexico CityMexico

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