Early Identification of Conflicts in the Chilean Fisheries and Aquaculture Sector Via Text Mining and Machine Learning Techniques

This project tackles challenges in Chile’s fishing and aquaculture sector, vital for both economic and social reasons. Aquaculture entrepreneurs and fishermen, spanning industrial to artisanal, depend on Chile’s rich hydrobiological resources. They face issues due to regulatory limits, designed to preserve species and ecosystem balance. The objective is to assess machine-learning algorithms with text mining to create an AI model. This model will help the Undersecretariat of Fisheries and Aquaculture anticipate conflicts via early alerts. The study employed the CRISP-DM methodology, focusing on a Neural Networks-based model, specifically the Multilayer Perceptron. This research surpassed its initial hypothesis, aiming for 70% accuracy in conflict classification. The model processed natural language from electronic media and Twitter, achieving 81.50% precision in conflict prediction. As a result, managers can now make more informed decisions to preemptively address conflicts with greater confidence.