The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse

The article presents the Meaning Intelligence Framework (MIF), a nine-dimension schema designed to enhance the understanding of Nigerian public discourse by distinguishing between surface sentiment and true communicative intent. It highlights the limitations of existing sentiment classification benchmarks and demonstrates that using the MIF with the Gemini 2.5 Flash model significantly improves register classification accuracy from 33.3% to 73.3% and increases the Meaning Intelligence Score by 5.4 points under schema-informed prompting. This framework and its accompanying calibration dataset are crucial for practitioners aiming to develop AI systems that accurately interpret nuanced language in diverse cultural contexts.