Supporting the Developmentof Team-Climate-Aware Collaborative Web Applications

  • Sebastian HeilEmail author
  • Marco Drechsel
  • Martin Gaedke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9114)


Collaborative web applications are widely used in enterprises to support work in virtual teams. Here, monitoring mood is essential for team managers to intervene and restore optimal working conditions to ensure work success. To retrieve mood information from natural language communication, sentiment analysis techniques are necessary. This, however, requires expertise and is time-consuming if done individually for each web application. In this paper, we present TCAS which supports developers of collaborative web applications to leverage sentiment analysis for team climate assessment.


Team climate assessment Sentiment analysis Collaboration 


  1. 1.
    Cappiello, C., Matera, M., Picozzi, M., Sprega, G., Barbagallo, D., Francalanci, C.: DashMash: A Mashup Environment for End User Development. In: Auer, S., Díaz, O., Papadopoulos, G.A. (eds.) ICWE 2011. LNCS, vol. 6757, pp. 152–166. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  2. 2.
    Dodds, P.S., et al.: Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLoS One 6(12) (12 2011)Google Scholar
  3. 3.
    Fisher, C.D.: Mood and emotions while working: missing pieces of job satisfaction? Journal of Organizational Behavior 21(2), 185–202 (2000)CrossRefGoogle Scholar
  4. 4.
    Kelly, J., Barsade, S.: Mood and Emotions in Small Groups and Work Teams. Organizational Behavior and Human Decision Processes 86(1), 99–130 (2001)CrossRefGoogle Scholar
  5. 5.
    Malinský, R., Jelínek, I.: Improvements of Webometrics by Using Sentiment Analysis for Better Accessibility of the Web. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 581–586. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  6. 6.
    Morinaga, S., et al.: Mining product reputations on the web. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 341–349. ACM, New York (2002)Google Scholar
  7. 7.
    Schiffer, B.: Agile Management Innovations: A Primer. Agile Product & Project Management 14(1) (2013)Google Scholar
  8. 8.
    Smith, F.J., Kerr, W.A.: Turnover factors as assessed by the exit interview. Journal of Applied Psychology 37(5), 352–355 (1953)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Technische Universität ChemnitzChemnitzGermany

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