Shaheen Syed
Thesis: Text Analytics for the 21st Century Fisheries
Since
the 90s, it has been well-known that unstructured and semi-structured
data constitute up to 90% of an organization’s data volume. The
unprecedented growth of the Web and social media since then has only
further increased the relative amount of unstructured and
semi-structured data.
A great way to analyze this largely unstructured textual data is
text analytics. Techniques such as sentiment analysis and named entity
recognition are heavily being used in all sorts of research institutions
and private companies. It enables the extraction of opinions on a given
subject, create structured data from unstructured data, uncover other
types of potential wealth and a lot more. It is a relatively new field
of study and some amazing insights have already been found in e.g.
Economics or Biology once they adopted text analytics.
My PhD research is aimed at implementing text analytics techniques
into the fisheries domain as a whole. That is, we are investigating to
what extent text analytics can be applied within the fisheries domain to
gain more in-depth knowledge about fisheries and its e.g. stakeholders
by utilizing quantitative computer science text mining techniques such
as natural language processing and machine learning. A high degree of
emphasis is placed on the investigation of different methods belonging
to the same technique. This makes the various studies somewhat more
explorative. However, some emphasis is placed on the predictive power of
text analytics for the fisheries domain in the final stages of this
PhD.
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