Data–driven Semiotics and Semiotics–driven Machine Learning

Nowadays there is a huge and growing variety of digital data. Despite
the obvious relevance for the humanities and the social sciences, these massive

quantities of data, usually defined as “big data”, are mainly selected and ana-
lyzed using computer science and statistics. The paper proposes a theoretical and

practical approach to the analysis of large quantities of data within the field of
semiotic analysis. The main claim is that semiotics should dialogue with IT and
statistics, that are essential to deal with the vastness and continuous variability of
data. In particular, machine learning might become really useful from a semiotic
perspective. In this work, we use a machine learning technique that is used in
Natural Language Processing (NLP), to create a vector space based on probabilities
of co–occurrences of words. In a distributional semantics perspective, this space is
interpreted as a representation of semantic relations among words. We present
then two directions in which we could intend the joint effort of semiotics and
machine learning. In the first case, we propose a case study of semiotics–driven
machine learning, in which we create a dataset starting from a semiotic analysis.
In the second case, we present an example of data–driven semiotics, were the
semiotic tools are used on an existing dataset, that was not build with semiotic
scopes. The two directions have not to be intended as a dichotomy but instead
as a part of a joint effort where semiotics interacts with machine learning and
machine learning interacts with qualitative analysis.