Leveraging Natural Language Processing Techniques to Analyze Agricultural Text Data: A Case Study on Risk Assessment Approaches

Abstract #

:

Detecting opportunities and threats from massive text data is a challenging task for

most. Traditionally, companies would rely mainly on structured data to detect and

predict risks, losing a huge amount of information that could be extracted from

unstructured text data.

Fortunately, industry 4.0 Technologies came to remedy this issue by innovating in

data extraction and processing techniques, allowing us to understand and make use

of Natural Language data and turning it into structures that a machine can process

and extract insight from.

This project aims to leverage natural language processing and machine learning

techniques to build a model capable of modeling uncertainties and evaluating the

risk level in each uncertainty cluster using massive text data.

This work focuses mainly on information extracted regarding the agricultural field.

Key words: Machine Learning, Natural Language Processing, Text data, Risk

analysis, Risk evaluation, Agricultural Risk Assessment, Unstructured data.