Skip to main content
Book cover

Urban Social Listening pp 15–25Cite as

Palgrave Macmillan

A (Short) History of Social Media Sentiment Analysis

Abstract

In this chapter, we introduce the concept of sentiment analysis as a way to study the prevalence of positive or negative sentiments in expressed attitudes and opinions. We review the ways researchers broadly have used sentiment analyses of Twitter data and other digital data and the established benefits and limits of this method for urban social science research. We then discuss how Twitter data and other similar forms of social media data have been applied in a wide variety of urban planning issues and projects across the globe.

Keywords

  • Social listening
  • Social media
  • Twitter
  • Microblogging

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1057/978-1-137-59491-4_2
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-1-137-59491-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Hardcover Book
USD   69.99
Price excludes VAT (USA)
Learn about institutional subscriptions

References

  • Antonelli, F., Azzi, M., Balduini, M., Ciuccarelli, P., Valle, E. D., & Larcher, R. (2014, May). City sensing: Visualising mobile and social data about a city scale event. In Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces, ACM, 337–338.

    Google Scholar 

  • Balduini, M., Della Valle, E., Dell’Aglio, D., Tsytsarau, M., Palpanas, T., & Confalonieri, C. (2013). Social listening of city scale events using the streaming linked data framework. In The semantic web–ISWC 2013 (pp. 1–16). Berlin/Heidelberg: Springer.

    CrossRef  Google Scholar 

  • Ben-Harush, O., Carroll, J.-A., & Marsh, B. (2012). Using mobile social media and GIS in health and place research. Continuum, 26(5), 715–730.

    CrossRef  Google Scholar 

  • Bertrand, K. Z., Bialik, M., Virdee, K., Gros, A., & Bar-Yam, Y. (2013, August 20). Sentiment in New York City: A high resolution spatial and temporal view. arXiv:1308.5010.

    Google Scholar 

  • Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C. M., & Dodds, P. S. (2012). Twitter reciprocal reply networks exhibit assortativity with respect to happiness. Journal of Computational Science, 3(5), 388–397.

    CrossRef  Google Scholar 

  • Bollen, J., Mao, H., & Pepe, A. (2011a). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 450–453.

    Google Scholar 

  • Bollen, J., Gonçalves, B., Ruan, G., & Mao, H. (2011b). Happiness is assortative in online social networks. Artificial Life, 17(3), 237–251.

    Google Scholar 

  • Cheng, Z., Caverlee, J., Lee, K., & Sui, D. Z. (2011). Exploring millions of footprints in location sharing services. ICWSM, Barcelona, Spain, 17 to 21 July 2011, pp. 81–88.

    Google Scholar 

  • Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2012). The livehoods project: Utilizing social media to understand the dynamics of a city. Sixth International AAAI Conference on Weblogs and Social Media, 58–65.

    Google Scholar 

  • Cui, A., Zhang, M., Liu, Y., & Ma, S. (2011). Emotion tokens: Bridging the gap among multilingual twitter sentiment analysis. 7th Asia Information Retrieval Societies Conference, AIRS.

    Google Scholar 

  • Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PloS One, 6(12), e26752. doi:10.1371/journal.pone.0026752.

    CrossRef  Google Scholar 

  • Eichstaedt, J. C., et al. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159–169.

    CrossRef  Google Scholar 

  • Evans-Cowley, J., & Hollander, J. (2010). The new generation of public participation: Internet-based participation tools. Planning, Practice & Research., 25(3), 397–408.

    CrossRef  Google Scholar 

  • Frias-Martinez, V., Soto, V., Hohwald, H., & Frias-Martinez, E. (2013). Sensing urban land use with Twitter activity. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.878&rep=rep1&type=pdf

    Google Scholar 

  • Fujisaka, T., Lee, R., & Sumiya, K. (2010). Exploring urban characteristics using movement history of mass mobile microbloggers. In Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications, ACM.

    Google Scholar 

  • Gao, Q., Abel, F., Houben, G. J., & Yu, Y. (2012). A comparative study of users’ microblogging behavior on Sina Weibo and Twitter. In User modeling, adaptation, and personalization (pp. 88–101). Berlin/Heidelberg: Springer.

    CrossRef  Google Scholar 

  • Gayo-Avello, D. (2011). Limits of electoral predictions using Twitter. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, AAAI, 490–493.

    Google Scholar 

  • Gayo-Avello, D. (2012). I wanted to predict elections with Twitter and all I got was this Lousy Paper—A balanced survey on election prediction using Twitter data. arXiv:1204.6441.

    Google Scholar 

  • Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-scale sentiment analysis for news and blogs. ICWSM, 7, 21.

    Google Scholar 

  • Gordon, J. (2013). Comparative geospatial analysis of Twitter sentiment data during the 2008 and 2012 US Presidential elections. Eugene: University of Oregon.

    Google Scholar 

  • Hale, S. A. (2014). Global connectivity and multilinguals in the Twitter network. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 833–842.

    Google Scholar 

  • Jungherr, A., Jürgens, P., & Schoen, H. (2012). Why the Pirate Party won the German election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. ‘Predicting elections with Twitter: What 140 characters reveal about political sentiment. Social Science Computer Review, 30, 229–234.

    CrossRef  Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. San Rafael: Morgan & Claypool Publishers.

    Google Scholar 

  • Lovelace, R., Malleson, N., Harland, K., & Birkin, M. (2014). Geotagged tweets to inform a spatial interaction model: A case study of museums. Leeds: School of Geography, University of Leeds.

    Google Scholar 

  • MacEachren, A. M., et al. (2011). Geo-twitter analytics: Applications in crisis management. 25th International Cartographic Conference.

    Google Scholar 

  • Magdy, A., Ghanem, T. M., Musleh, M., & Mokbel, M. F. (2014). Exploting geo-tagged tweets to understand local language diversity. Proceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data, ACM. 2.

    Google Scholar 

  • Mearns, G., Simmonds, R., Richardson, R., Turner, M., Watson, P., & Missier, P. (2014). Tweet my street: A cross-disciplinary collaboration for the analysis of local Twitter data. Future Internet, 6(2), 378–396.

    CrossRef  Google Scholar 

  • Metaxas, P. T., & Mustafaraj, E. (2012). Social media and the elections. Policy Forum, 338, 472–473.

    Google Scholar 

  • Mitchell, L., Harris, K. D., Frank, M. R., Dodds, P. S., & Danforth, C. M. (2013). The Geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS One, 8(5), e64417.

    CrossRef  Google Scholar 

  • Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A. (2013). The Twitter of Babel: Mapping world languages through microblogging platforms. PloS One. doi:10.1371/journal.pone.0061981.

    Google Scholar 

  • O’Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11, 122–129.

    Google Scholar 

  • Poblete, B., Garcia, R., Mendoza, M., & Jaimes, A. (2011). Do all birds tweet the same? Characterizing Twitter around the world. In Proceedings of the 20th ACM international conference on information and knowledge management, ACM, 1025–1030.

    Google Scholar 

  • Quercia, D., Ellis, J., Capra, L., & Crowcroft, J. (2012). Tracking gross community happiness from tweets. 15th ACM Conference on Computer Supported Cooperative Work (CSCW). Seattle.

    Google Scholar 

  • Sakaki, T., Okazaki, M., & Matsuo, Y. (2010, April 26–30). Earthquake shakes Twitter users: Real-time event detection by social sensors. WWW2010, Raleigh.

    Google Scholar 

  • Sang, E. T. K., & Bos, J. (2012). Predicting the 2011 Dutch senate election results with Twitter. In Proceedings of the Workshop on Semantic Analysis in Social Media, 53–60.

    Google Scholar 

  • Schroeter, R., & Houghton, K. (2011). Neo-planning: Location-based social media to engage Australia’s new digital locals. Proceedings of Planning Institute of Australia National Conference 2011. Planning Institute of Australia.

    Google Scholar 

  • Schweitzer, L. (2014). Planning and social media: A case study of public transit and stigma on Twitter. Journal of the American Planning Association, 80(3), 218–238.

    CrossRef  Google Scholar 

  • Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, AAAI, 178–184.

    Google Scholar 

  • Wakamiya, S., Lee, R., & Sumiya, K. (2011). Crowd-based urban characterization: Extracting crowd behavioral patterns in urban areas from twitter. In Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks, ACM.

    Google Scholar 

  • Wilkinson, D., & Thelwall, M. (2012). Trending Twitter topics in English: An international comparison. Journal of the American Society for Information Science and Technology, 63(8), 1631–1646.

    CrossRef  Google Scholar 

  • Zhang, X., Fuehres, H., & Gloor, P. A. (2011a). Predicting stock market indicators through Twitter: ‘I hope it is not as bad as I fear. Procedia – Social and Behavioral Sciences, 26, 55–62.

    CrossRef  Google Scholar 

  • Zhang, X., Fuehres, H., & Gloor, P. A. (2011b). Predicting asset value through Twitter Buzz. In J. Altmann et al. (Eds.), Advances in collective intelligence 2011, AISC 113, 23–34.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tufts University, Medford, Massachusetts, USA

    Justin B. Hollander, Cara Foster-Karim, Andrew Wiley & Dibyendu Das

  2. Urban Planner and Sociologist, Cambridge, USA

    Erin Graves

  3. University of Massachusetts Amherst, Amherst, Massachusetts, USA

    Henry Renski

Authors
  1. Justin B. Hollander
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erin Graves
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henry Renski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cara Foster-Karim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew Wiley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dibyendu Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Hollander, J.B., Graves, E., Renski, H., Foster-Karim, C., Wiley, A., Das, D. (2016). A (Short) History of Social Media Sentiment Analysis. In: Urban Social Listening. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59491-4_2

Download citation