Simple TF·IDF is not the best you can get for regionalism classification

In broadly spoken languages such as English or Spanish, there are words akin to a particular region. For example, there are words typically used in the UK such as cooker, while stove is preferred for that concept in the US. Identifying the particular words a region cultivates involves discriminating them from the set of common words to all regions. This yields the problem where a term's frequency should be salient enough to be considered of importance, while being a common term tames this salience. This is the known problem of Term Frequency versus the Inverse Document Frequency; nevertheless, typical TF·IDF applications do not include weighting factors. In this work we propose several alternative formulae empirically, and then we conclude that we need to dig in a broader search space; thereby, we propose using Genetic Programming to find a suitable expression composed of TF and IDF terms that maximizes the discrimination of such terms given a reduced bootstrapping set of examples labeled for each region (400). We present performance examples for the Spanish variations across the Americas and Spain.