  Short texts are omnipresent in real-time news, social network commentaries,
etc. Traditional text representation methods have been successfully applied to
self-contained documents of medium size. However, information in short texts is
often insufficient, due, for example, to the use of mnemonics, which makes them
hard to classify. Therefore, the particularities of specific domains must be
exploited. In this article we describe a novel system that combines Natural
Language Processing techniques with Machine Learning algorithms to classify
banking transaction descriptions for personal finance management, a problem
that was not previously considered in the literature. We trained and tested
that system on a labelled dataset with real customer transactions that will be
available to other researchers on request. Motivated by existing solutions in
spam detection, we also propose a short text similarity detector to reduce
training set size based on the Jaccard distance. Experimental results with a
two-stage classifier combining this detector with a SVM indicate a high
accuracy in comparison with alternative approaches, taking into account
complexity and computing time. Finally, we present a use case with a personal
finance application, CoinScrap, which is available at Google Play and App
Store.
