  A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning problem, whereas others are specifically designed to tackle a
particular task, using complex and computational expensive processes such as
lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
propose a minimalistic and wide system able to tackle text classification tasks
independent of domain and language, namely microTC. It is composed by some easy
to implement text transformations, text representations, and a supervised
learning algorithm. These pieces produce a competitive classifier even in the
domain of informally written text. We provide a detailed description of microTC
along with an extensive experimental comparison with relevant state-of-the-art
methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
microTC obtained the best performance in 20 datasets while achieves competitive
results in the remaining 10. The compared datasets include several problems
like topic and polarity classification, spam detection, user profiling and
authorship attribution. Furthermore, it is important to state that our approach
allows the usage of the technology even without knowledge of machine learning
and natural language processing.
