AfriSenti

Meaning Intelligence Framework addresses context failure in AI processing of Nigerian public discourse

Researchers introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema designed to separate surface sentiment from true communicative intent in Nigerian public discourse. The paper argues that AI systems fail on Nigerian language data primarily due to context failure rather than translation failure, as pragmatic meaning shifts with speaker, audience, and situation. Evaluating Gemini 2.5 Flash on a 30-item calibration dataset, they find zero-shot register classification accuracy of 33.3% rising to 73.3% with schema-informed prompting, demonstrating large gains from structured in-context guidance. The framework and calibration set are released publicly to support reproducibility.