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Future of Information and Communication Conference

FICC 2020: Advances in Information and Communication pp 678–691Cite as

A Text Classification Model to Identify Performance Bonds Requirement in Public Bidding Notices

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1130)

Abstract

A performance bond is a means of guaranteeing that a product that will be delivered by the seller in a timely and workmanlike manner. To verify whether a performance bond has been laid down in a bidding notice, an assessment through an internal auditing process is made periodically, but it can be costly and take longer than the available time to conclude an audit engagement. We propose to explore and apply algorithms to create a model able to identify bidding notices that demand performance bonds, to make the assessment process more efficient. We applied Four different classification algorithms (SVM, kNN, Random Forest, and Naive Bayes) on two different vector space representations (term frequency and term frequency inverse-document frequency). Random Forest with term-frequency produced the best model, which achieved \(F_{1}\)-score of 0.933 on the test set. The promising results show the model created could help decrease the time and effort spent on verifying the performance bonds requirement on bidding notices.

Keywords

  • Public bidding notice
  • Performance bonds
  • Text mining
  • Classification
  • Machine learning
  • Random forest
  • SVM
  • K-Nearest Neighbors
  • Multinomial Naive Bayes
  • Term frequency
  • Inverse document frequency

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  • DOI: 10.1007/978-3-030-39442-4_50
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Notes

  1. 1.

    Lei 8.666, de 21 de junho de 1993 – http://www.planalto.gov.br/ccivil_03/Leis/l8666cons.htm.

  2. 2.

    https://www.comprasgovernamentais.gov.br/.

  3. 3.

    http://www.xpdfreader.com/.

  4. 4.

    http://python-docx.readthedocs.io/en/latest/.

  5. 5.

    https://www.nltk.org/.

  6. 6.

    http://scikit-learn.org/stable/index.html.

  7. 7.

    http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction.

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Authors and Affiliations

  1. University of Brasília (UnB), Brasília, Brazil

    Urias Cruz da Cunha, Ricardo Silva Carvalho & Alexandre Zaghetto

Authors
  1. Urias Cruz da Cunha
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  2. Ricardo Silva Carvalho
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  3. Alexandre Zaghetto
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Corresponding authors

Correspondence to Urias Cruz da Cunha , Ricardo Silva Carvalho or Alexandre Zaghetto .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Prof. Rahul Bhatia

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da Cunha, U.C., Carvalho, R.S., Zaghetto, A. (2020). A Text Classification Model to Identify Performance Bonds Requirement in Public Bidding Notices. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_50

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