Term Weighting Vs. Logistic Regression Performance on E-Commerce Data

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Text categorization can become a very difficult problem to solve in many cases. However many text categorization algorithms have been developed in the history of computer science, they are not always as accurate as we expect. Some of them are highly accurate in special cases while others perform well in different cases. In this work, we are comparing two famous methods in text categorization; the first one is the well-known term weighting algorithm and the second one is the logistic regression algorithm. All the dataset is got from our previous start-up named “Ume Market Network” which was an online peer-to-peer e-commerce system, and was synchronized with Facebook sales groups. Every offer in this dataset should be categorized as a sale/purchase offer; therefore, the problem is a classical binary categorization on a text dataset of formal as well as colloquial expressions in English, Italian, and German languages. After overcoming all the ambiguities the logistic regression algorithm outperformed the term weighting algorithm by around 25% in acuracy.


  • Keywords


    Machine Learning; Logistic Regression; Supervised Term Weighting; Text Categorization; Multiclass Classification

  • References


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Article ID: 22738
 
DOI: 10.14419/ijet.v7i4.35.22738




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