Abstract

This thesis reviews and evaluates intelligent methods for identifying customer needs in user-generated content (UGC). It first surveys prior work and shows that many studies share generic goals yet overlook the complexity and taxonomy of needs in their evaluation setups. To clarify scope, the thesis distinguishes between using Machine Learning (ML) as a tool to support marketing workflows and treating customer-needs identification itself as an Natural Language Processing (NLP) task with clear definitions and constructs. Building on this perspective, a large experimental study assesses Transformer-based models for generalizability, robustness, fairness, and sample efficiency across varied settings. Results indicate competitive accuracy, with gains in F1 up to 18% over baselines, but also consistent limitations: shared error patterns, difficulty with rare or unseen needs, reliance on lexical cues that weakens cross-domain performance, and no guaranteed gains in sample efficiency from larger models. Cross-domain results benefit most from richer, diverse domain training, while adding more in-domain data does not improve transfer. Beyond technical metrics, the thesis highlights adoption barriers, costs, data constraints, task complexity, and ethical considerations and argues for evaluation frameworks that reflect taxonomy, transparency, and fairness. It concludes with practical guidance that bridges marketing theory and NLP practice to support responsible, reproducible deployment.