Integrated Text Analytics Framework for Business Risk Identification

Abstract

Enterprises generate large volumes of unstructured textual data that contain valuable risk-related information. Extracting reliable indicators from such data remains challenging. This paper proposes an integrated text analytics framework combining statistical feature modeling, semantic embedding, and ensemble classification. Domain-specific lexicons and sampling strategies are introduced to improve model robustness. Experiments on corporate disclosure datasets confirm improved prediction accuracy and interpretability.