arXiv:2606.20255v1 Announce Type: new Abstract: We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent. Existing benchmarks for Nigerian languages, including NaijaSenti and AfriSenti, treat sentiment classification as a three-way polarity task (positive, negative, neutral). We argue that the dominant failure mode of AI systems on Nigerian discourse is not translation failure but context failure: the same utterance carries opposite pragmatic force depen
The proliferation of AI systems interacting with diverse global discourse highlights the immediate need for frameworks that account for cultural and linguistic nuances beyond simplistic sentiment analysis.
This framework addresses a critical limitation in AI's understanding of non-Western contexts, impacting its effectiveness and trustworthiness in regions like Nigeria, and preventing misinterpretation of public sentiment and intent.
The Meaning Intelligence Framework introduces a more sophisticated approach to analyzing Nigerian public discourse, shifting focus from surface sentiment to underlying communicative intent, thereby improving AI's contextual understanding.
- · Nigerian AI researchers
- · AI systems focused on diverse language models
- · African tech companies
- · AI models reliant solely on sentiment analysis
- · Developers ignoring cultural context in AI applications
Improved accuracy and reliability of AI applications in analyzing Nigerian public discourse.
Potential for development of more culturally intelligent AI systems globally, moving beyond Western-centric data and analysis.
Enhanced trust in AI among diverse populations, fostering greater adoption and reducing the risk of 'context failure' leading to social or political misinterpretations.
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