Addressing Topic Modeling with a Multi-Objective Optimization Approach based on Swarm Intelligence

Abstract

Topic modeling is a growing eld within the area of text analysis that extracts underlying topics from document collections. Several objectives can be simultaneously considered when designing an approach for topic modeling. A multi-objective optimization approach based on the swarm intelligence of a bee colony (MOABC, Multi-Objective Arti cial Bee Colony) has been designed, implemented, and tested. This new approach has been evaluated by using documents from the Reuters-21578 and TagMyNews datasets. Three objective functions (coherence, coverage, and perplexity) and three multiobjective metrics (hypervolume, set coverage, and distance to the ideal point have been considered in two topic scenarios. Results show that MOABC provides relevant improvements with respect to LDA (Latent Dirichlet Allocation, the most used approach for topic modeling) and MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition, the only multi-objective approach published to date). This demonstrates that the multi-criteria nature of topic modeling should be exploited with multi-objective optimization approaches.