.png){kind=icon}
.png){kind=icon}
Return
Addressing topic modeling with a multi-objective optimization approach based on swarm intelligence
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107113.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Topic modeling is a growing field 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 Artificial 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 multi objective 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. (C) 2021 Elsevier B.V. All rights reserved.Y
Keywords:
Artificial bee colony
Evolutionary computing
Latent Dirichlet allocation
Multi-objective optimization
Text analysis
Topic modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W