Benchmarking Sparse and Dense Models for Deception Detection in Negotiation: A Context-Aware and Imbalance-Sensitive Approach

Automatic detection of deceptive intent in negotiation dialogue remains difficult because deceptive utterances are rare, context-dependent, and pragmatically subtle. This study develops a deployment-oriented evaluation pipeline for negotiation analytics using the Diplomacy corpus and compares sparse, dense, and imbalance-aware neural models under a unified protocol. The pipeline integrates context-window benchmarking, validation-based threshold selection, 10-seed robustness analysis, model-agnostic explanation case studies, and controlled perturbation stress tests. Across binary speaker-intention and receiver-perception tasks, contextualized inputs consistently outperform isolated utterances, confirming that deception-related interpretation is inherently sequential. The sparse term frequency–inverse document frequency (TF-IDF) model remains the strongest and most efficient overall benchmark, whereas stronger imbalance-aware neural baselines can improve minority deceptive-instance sensitivity at substantially higher computational cost. Error analysis further shows that socially mediated deception-quadrant prediction is markedly harder than direct binary in-tent prediction, with many failures collapsing toward majority straightforward cases. Controlled perturbation tests show that sparse modeling is especially stable under light-weight surface corruption, while neural robustness remains architecture-dependent. The main contribution is therefore a practical decision framework for selecting among efficient sparse monitoring, dense baselines, and minority-sensitive neural detection under operational constraints.