Emerging Technologies for Developing Countries. Second EAI International Conference, AFRICATEK 2018, Cotonou, Benin, May 29–30, 2018, Proceedings

Research Article

Vector Space Model of Text Classification Based on Inertia Contribution of Document

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  • @INPROCEEDINGS{10.1007/978-3-030-05198-3_14,
        author={Demba Kand\^{e} and Fod\^{e} Camara and Reine Marone and Samba Ndiaye},
        title={Vector Space Model of Text Classification Based on Inertia Contribution of Document},
        proceedings={Emerging Technologies for Developing Countries. Second EAI International Conference, AFRICATEK 2018, Cotonou, Benin, May 29--30, 2018, Proceedings},
        proceedings_a={AFRICATEK},
        year={2018},
        month={12},
        keywords={Vector space model Classification Text mining Term weighting scheme},
        doi={10.1007/978-3-030-05198-3_14}
    }
    
  • Demba Kandé
    Fodé Camara
    Reine Marone
    Samba Ndiaye
    Year: 2018
    Vector Space Model of Text Classification Based on Inertia Contribution of Document
    AFRICATEK
    Springer
    DOI: 10.1007/978-3-030-05198-3_14
Demba Kandé1,*, Fodé Camara2,*, Reine Marone, Samba Ndiaye1
  • 1: Cheikh Anta Diop University
  • 2: Alioune Diop University
*Contact email: demba4.kande@ucad.edu.sn, fode.camara@uadb.edu.sn

Abstract

The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtening with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.