Fine-Tuning Topic Modelling: A Coherence-Focused Analysis of Correlated Topic Models

Abstract

The Correlated Topic Model (CTM) is a widely used approach for topic modelling that accounts for correlations among topics. This study investigates the effects of hyperparameter tuning on the model's ability to extract meaningful themes from a corpus of unstructured text. Key hyperparameters examined include learning rates (0.1, 0.01, 0.001), the number of topics (3, 5, 7, 10), and the number of top words (10, 20, 30, 40, 50, 80, 100). The Adam optimizer was used for model training, and performance was evaluated using the coherence score (c_v), a metric that assesses the interpretability and coherence of the generated topics. The dataset comprised 100 articles, and results were visualized using line plots and heatmaps to highlight performance trends. The highest coherence score of 0.803 was achieved with three topics and 10 top words. The findings demonstrate that fine-tuning hyperparameters significantly improves the model's ability to generate coherent and interpretable topics, resulting in more accurate and insightful outcomes. This research underscores the importance of parameter optimization in enhancing the effectiveness of CTM for topic modelling applications.