  In recent years, the interest in Big Data sources has been steadily growing
within the Official Statistic community. The Italian National Institute of
Statistics (Istat) is currently carrying out several Big Data pilot studies.
One of these studies, the ICT Big Data pilot, aims at exploiting massive
amounts of textual data automatically scraped from the websites of Italian
enterprises in order to predict a set of target variables (e.g. e-commerce)
that are routinely observed by the traditional ICT Survey. In this paper, we
show that Deep Learning techniques can successfully address this problem.
Essentially, we tackle a text classification task: an algorithm must learn to
infer whether an Italian enterprise performs e-commerce from the textual
content of its website. To reach this goal, we developed a sophisticated
processing pipeline and evaluated its performance through extensive
experiments. Our pipeline uses Convolutional Neural Networks and relies on Word
Embeddings to encode raw texts into grayscale images (i.e. normalized numeric
matrices). Web-scraped texts are huge and have very low signal to noise ratio:
to overcome these issues, we adopted a framework known as False Positive
Reduction, which has seldom (if ever) been applied before to text
classification tasks. Several original contributions enable our processing
pipeline to reach good classification results. Empirical evidence shows that
our proposal outperforms all the alternative Machine Learning solutions already
tested in Istat for the same task.
