Skip to main content
SpringerLink
Log in

A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models

  • Conference paper
  • First Online:
Book cover Product-Focused Software Process Improvement (PROFES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10611))

Abstract

Background: In the context of a larger research project, we plan to automatically extract user needs (i.e., functional requirements) from online open sources and classify them using the principles of the Kano model. In this paper, we present a two-step method for automatically transforming feature related text extracted from online open sources into inputs for Kano-like models. Goal: The problem we are facing is how to transform requirements and related sentiments extracted from raw texts collected from an online open source into the input format required by our Kano-like models. To solve this problem, we need a method that transforms requirements and related sentiments into a format that corresponds to answers that would be given to either the functional or dysfunctional question of the Kano method on a specific requirement. Method: We propose a method consisting of two steps. In the first step, we apply machine learning methods to decide whether a text line extracted from an online open source corresponds to an answer of the functional or dysfunctional question asked in the Kano method. In the second step, we use a dictionary-based method to classify the sentiment of each statement such that we can assign an answer value to each text line previously classified as functional or dysfunctional. We implemented our method in the R language. We evaluate the accuracy of the proposed method using simulation. Result: Based on the simulation results, we found the overall accuracy of our method is 65%. We also found that data sources such as app store reviews are better suited to our analysis than question/answer sources such as Stack Overflow. Conclusion: The method we proposed can be used to automatically transform feature-related text into inputs for Kano-like models but performance improvements are needed.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    O = One-dimensional Quality, A = Attractive Quality, M = Must-be Quality, I = Indifferent Quality, R = Reverse Quality.

  2. 2.

    Accuracy = (TF + TD)/(TF + FF + FD + TD).

  3. 3.

    FPV = TF/(TF + FF).

  4. 4.

    DPV = TD/(FD + TD).

  5. 5.

    https://figshare.com/s/d13b6f16738190d7b935.

References

  1. Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Proceedings of the Asia Pacific Finance Association Annual Conference (APFA), vol. 35, p. 43 (2001)

    Google Scholar 

  2. Morinaga, S., Yamanishi, K., Tateishi, K., et al.: Mining product reputations on the web. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 341–349. ACM (2002)

    Google Scholar 

  3. Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, vol. 1, p. 6 (2001)

    Google Scholar 

  4. Wiebe, J.: Learning subjective adjectives from corpora. In: AAAI/IAAI, pp. 735–740 (2000)

    Google Scholar 

  5. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  6. Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, pp. 70–77. ACM (2003)

    Google Scholar 

  7. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  8. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)

    Google Scholar 

  9. Kano, N., Seraku, N., Takahashi, F., et al.: Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control 14, 39–48 (1984)

    Google Scholar 

  10. Yin, H., Pfahl, D.: Evaluation of Kano-like models defined for using data extracted from online sources. In: Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T. (eds.) PROFES 2016. LNCS, vol. 10027, pp. 539–549. Springer, Cham (2016). doi:10.1007/978-3-319-49094-6_39

    Chapter  Google Scholar 

  11. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)

    Google Scholar 

  12. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 129–136. Association for Computational Linguistics (2003)

    Google Scholar 

  13. Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: AAAI, vol. 4, pp. 761–769 (2004)

    Google Scholar 

  14. Lin, C., He, Y., Everson, R., et al.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24(6), 1134–1145 (2012)

    Article  Google Scholar 

  15. Rao, Y., Li, Q., Mao, X., et al.: Sentiment topic models for social emotion mining. Inf. Sci. 266(5), 90–100 (2014)

    Article  Google Scholar 

  16. Shah, F.A., Sabanin, Y., Pfahl, D.: Feature-based evaluation of competing apps. In: Proceedings of the International Workshop on App Market Analytics, WAMA 2016, pp. 15–21. ACM, New York (2016)

    Google Scholar 

  17. Ganapathibhotla, M., Liu, B.: Mining opinions in comparative sentences. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 241–248. Association for Computational Linguistics (2008)

    Google Scholar 

  18. Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)

    Article  Google Scholar 

  19. Reagan, A., Tivnan, B., Williams, J.R., et al.: Benchmarking sentiment analysis methods for large-scale texts: a case for using continuum-scored words and word shift graphs. Comput. Sci. (2015)

    Google Scholar 

  20. Ku, L.W., Wu, T.H., Lee, L.Y., et al.: Construction of an evaluation corpus for opinion extraction. In: NTCIR, pp. 513–520 (2005)

    Google Scholar 

  21. Dasgupta, S., Ng, V.: Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification. In: Joint Conference of the, Meeting of the ACL and the, International Joint Conference on Natural Language Processing of the AFNLP: Volume, pp. 701–709. Association for Computational Linguistics (2009)

    Google Scholar 

  22. Socher, R., Perelygin, A., Wu, J.Y., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the conference on empirical methods in natural language processing (EMNLP). 1631, 1642 (2013)

    Google Scholar 

  23. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)

    Google Scholar 

  24. HowNet knowledge Database (2016). http://www.keenage.com/html/e_index.html. Accessed 2 Feb 2017

  25. Skinner, C.J.: Probability proportional to size (PPS) sampling. In: Encyclopedia of Statistical Sciences (1983)

    Google Scholar 

  26. Recursive Neural Tensor Network (2017). http://nlp.stanford.edu/sentiment/index.html. Accessed 2 Feb 2017

  27. Aly, M.: Survey on multiclass classification methods. Neural Netw., 1–9 (2005)

    Google Scholar 

  28. Mustasfa, B.A.: Classifying software requirements using Kano’s model to optimize customer satisfaction. In: SoMeT, pp. 271–279 (2014)

    Google Scholar 

  29. Nascimento, P., Aguas, R., Schneider, D., et al.: An approach to requirements categorization using Kano’s model and crowds. In: 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 387–392. IEEE (2012)

    Google Scholar 

  30. Yin, H.: A study plan: open innovation based on internet data mining in software engineering. In: Proceedings of the 2015 International Conference on Software and System Process. ACM (2015)

    Google Scholar 

Download references

Acknowledgement

The research was supported by the institutional research grant IUT20-55 of the Estonian Research Council. In addition, Huishi Yin was funded by the European Regional Development Fund for Higher Education.

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia

    Huishi Yin & Dietmar Pfahl

Authors
  1. Huishi Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dietmar Pfahl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huishi Yin .

Editor information

Editors and Affiliations

  1. University of Innsbruck, Innsbruck, Austria

    Michael Felderer

  2. Technical University Munich, Garching, Germany

    Daniel Méndez Fernández

  3. Brunel University London, Uxbridge, United Kingdom

    Burak Turhan

  4. Pontifical Catholic University of Rio de, Rio de Janeiro, Brazil

    Marcos Kalinowski

  5. University College London, London, United Kingdom

    Federica Sarro

  6. Vienna University of Technology, Vienna, Austria

    Dietmar Winkler

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Yin, H., Pfahl, D. (2017). A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds) Product-Focused Software Process Improvement. PROFES 2017. Lecture Notes in Computer Science(), vol 10611. Springer, Cham. https://doi.org/10.1007/978-3-319-69926-4_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69926-4_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69925-7

  • Online ISBN: 978-3-319-69926-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation