  This paper focuses on a comparative evaluation of the most common and modern
methods for text classification, including the recent deep learning strategies
and ensemble methods. The study is motivated by a challenging real data
problem, characterized by high-dimensional and extremely sparse data, deriving
from incoming calls to the customer care of an Italian phone company. We will
show that deep learning outperforms many classical (shallow) strategies but the
combination of shallow and deep learning methods in a unique ensemble
classifier may improve the robustness and the accuracy of "single"
classification methods.
