The aim of text categorization is to assign documents to predefined categories as accurately as possible. We are within the supervised learning framework, with a categorical target attribute, often binary. The originality lies in the nature of the input attribute, which is a textual document. It is not possible to implement predictive methods directly, it is necessary to go through a data preparation phase.
In this tutorial, we will describe a text categorization process in Python using mainly the text mining capabilities of the scikit-learn package, which will also provide data mining methods (logistics regression). We want to classify SMS as "spam" (spam, malicious) or "ham" (legitimate). We use the “SMS Spam Collection v.1” dataset.
Keywords: text mining, document categorization, corpus, bag of words, f1-score, recall, precision, dimensionality reduction, variable selection, logistic regression, scikit learn, python
Tutorial: Spam identification
Dataset: Corpus and Python program
References:
Almeida, T.A., Gómez Hidalgo, J.M., Yamakami, "A. Contributions to the Study of SMS Spam Filtering: New Collection and Results", in Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.
