Skip to main content
SpringerLink
Log in

SentiProMo: A Sentiment Analysis-Enabled Social Business Process Modeling Tool

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

Included in the following conference series:

Abstract

SentiProMo is a novel social business process modeling tool enabled by sentiment analysis. We designed and developed SentiProMo for supporting social business processes management to enhance the business process management (BPM) lifecycle. In particular, we socially improve the BPM lifecycle in the process analysis stage by capturing and processing stakeholder’s opinions regarding the tasks within a business process. By taking a social information systems perspective, SentiProMo transforms these opinions with sentiment analysis and classifies them into positive and negative feedback. The aim is to support the business analysts for redesigning a business process by considering the sentiment-analyzed opinions for designing the to-be process. We illustrate the current SentiProMo’s capabilities with a simple process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The software tool, training data, and, testing data is available at https://sites.google.com/view/sentipromo.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences.

  3. 3.

    http://help.sentiment140.com/for-students/.

References

  1. Chen, H., Zimbra, D.: Ai and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)

    Article  Google Scholar 

  2. Das, M., Chow, L.: Next generation bpm suites: social and collaborative. Social BPM-Work, Planning and Collaboration Under the Impact of Social Technology, pp. 193–208 (2011)

    Google Scholar 

  3. Dumas, M., La Rosa, M., Mendling, J., Hajo, R.: Fundamentals of Business Process Management, 2nd edn. Springer-Verlag, Berlin (2018)

    Book  Google Scholar 

  4. Erol, S., et al.: Combining bpm and social software: contradiction or chance? J. Softw. Maintenance Evol. Res. Pract. 22(6–7), 449–476 (2010)

    Article  Google Scholar 

  5. Hrastnik, J., Cardoso, J.S., Kappe, F.: The business process knowledge framework. In: ICEIS (3), pp. 517–520 (2007)

    Google Scholar 

  6. Kotzias, D., Denil, M., De Freitas, N., Smyth, P.: From group to individual labels using deep features. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 597–606. ACM (2015)

    Google Scholar 

  7. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012)

    Chapter  Google Scholar 

  8. Pflanzl, N., Vossen, G.: Human-oriented challenges of social bpm: an overview. Enterprise Modelling and Information Systems Architectures (EMISA 2013) (2013)

    Google Scholar 

  9. Pucher, M.J.: How to link bpm governance and social collaboration through an adaptive paradigm. Social BPM-Work, Planning and Collaboration Under the Impact of Social Technology, pp. 57–76 (2011)

    Google Scholar 

  10. Schmidt, R., Alt, R., Nurcan, S.: Introduction to the minitrack on social information systems. In: Proceedings of the 52nd Hawaii International Conference on System Sciences (2019)

    Google Scholar 

  11. Aalst, W.: Data Science in Action. Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CAMPUS 02 University of Applied Sciences, IT and Business Informatics, Graz, Austria

    Egon Lüftenegger & Selver Softic

Authors
  1. Egon Lüftenegger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selver Softic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Egon Lüftenegger .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lüftenegger, E., Softic, S. (2020). SentiProMo: A Sentiment Analysis-Enabled Social Business Process Modeling Tool. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66498-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation