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
Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: ICWSM (2011)
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)
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)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12 (2009)
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© 2013 Springer International Publishing Switzerland
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Martínez, V., Gonzílez, V.M. (2013). Sentiment Characterization of an Urban Environment via Twitter. In: Urzaiz, G., Ochoa, S.F., Bravo, J., Chen, L.L., Oliveira, J. (eds) Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction. Lecture Notes in Computer Science, vol 8276. Springer, Cham. https://doi.org/10.1007/978-3-319-03176-7_54
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DOI: https://doi.org/10.1007/978-3-319-03176-7_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03175-0
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