Performance Complementarity in Topic Modeling: Strategic Algorithm Selection for Business Intelligence in African Markets

Darren Craig Roos

Keywords: Natural language processing, Topic modelling, Performance analysis, Performance complementarity

Abstract:

Topic modeling algorithms are increasingly vital for business intelligence in African markets, where understanding diverse textual data from multiple languages and contexts is crucial for informed decision-making.
However, practitioners face the persistent question: which algorithm should be used for their specific business application?
Through a comprehensive evaluation of eleven contextual topic modeling algorithms across ten diverse datasets and four performance metrics, we demonstrate that performance complementarity—rather than algorithmic superiority—characterizes this domain.
Our findings reveal that in 84\% of evaluation scenarios, all algorithms are Pareto optimal, each offering unique strengths that cannot be dominated by others.
This evidence challenges the common practice of seeking a single "best" algorithm and instead advocates for strategic algorithm selection based on specific business requirements and data characteristics.
For African businesses navigating complex multilingual markets and diverse data sources, understanding these performance trade-offs is essential for deploying effective AI-driven topic modeling solutions.

Submission Number: 5