Machine Learning (ML) and Artificial Intelligence (AI) Approaches to Unstructured Data

This study explores the application of machine learning (ML) and artificial intelligence (AI) techniques to analyze unstructured textual data, focusing on topic modeling, sentiment detection, and behavioral prediction. We employ multinomial document models and unsupervised learning strategies to extract latent topics and evaluate the emotional and conversational drivers behind social media posts. A major contribution is the implementation of Behavior Dirichlet Probability Model (BDPM) which analyzes user moods and behaviors through unstructured textual data. The results validate the hypothesis of the model's ability to identify and guess behavior patterns with high accuracy, providing actionable insights for digital marketing strategies, techniques to enhance user interaction and mental wellness evaluation.