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.
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Acknowledgment
We would like to express our sincere thanks to the CEA-MITIC
(Centre d’Excellence Africain en Mathématiques, Informatique et Tic) who financed our research by paying the publication fees of the 2papiers that we published in Africatek2018. The CEA-MITIC, located at the UFR of Applied Sciences and Technology (UFR SAT) of the Gaston Berger University (UGB) of Saint-Louis in Senegal, is a consortium of university institutions in Senegal and subregion of Senegal, research institutions and national, regional and international companies involved in the ICT sector.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kandé, D., Camara, F., Marone, R.M., Ndiaye, S. (2019). Vector Space Model of Text Classification Based on Inertia Contribution of Document. In: Zitouni, R., Agueh, M. (eds) Emerging Technologies for Developing Countries. AFRICATEK 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-030-05198-3_14
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DOI: https://doi.org/10.1007/978-3-030-05198-3_14
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