Shaheen SyedThesis: 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.