Hybrid DeepSentX Framework for AI-Driven Requirements Insight and Risk Prediction in Multilingual Sports Using Natural Language Processing

Abstract

Engineering teams need timely signals about evolving requirements and release risk, yet multilingual fan discourse around live sports is noisy, code-switched, and saturated with sarcasm and event-driven drift. We present Hybrid DeepSentX, an AI-driven framework that converts crowd commentary into actionable requirements insight and sprint-level risk scores. The pipeline couples multilingual transformer encoders with an inductive GraphSAGE conversation graph to inject relational context across posts, and adds a reinforcement learner whose reward is shaped to prioritize correct decisions on sarcasm-heavy items and rapidly shifting events. We assembled a million-plus posts from X, Reddit, and sports forums and evaluated the framework against strong baselines, including BERT, long short-term memory, support-vector machines, and recent hybrid models, with significance tests, calibration analysis, ablations, and efficiency profiling. DeepSentX achieved higher macro-averaged accuracy and F1 on code-switched and sarcastic subsets, reduced missed risk flags, and produced developer-facing artefacts that directly support backlog grooming and defect triage. Relative to prior hybrids that combine transformers with either graph reasoning or reinforcement alone, our contributions are fourfold: (i) a unified multilingual design that integrates transformer, graph, and reinforcement components for sarcasm and drift robustness, (ii) an annotated multi-platform corpus with explicit code switching and sarcasm labels and per platform language balance, (iii) a rigorous comparative study reporting accuracy, calibration, latency, memory, and parameter count, and (iv) deployment artefacts that turn model outputs into requirement clusters and sprint risk scores suitable for continuous planning.