TextNetTopics (Yousef et al. in Front Genet 13:893378, 2022. https://doi.org/10.3389/fgene.2022.893378 ) is a recently developed approach that performs text classification-based topics (a topic is a group of terms or words) extracted from a Latent Dirichlet Allocation topic modeling as features rather than individual words. Following this approach enables TextNetTopics to fulfill dimensionality reduction while preserving and embedding more thematic and semantic information into the text document representations. In this article, we introduced a novel approach, the Ensemble Topic Model for Topic Selection (ENTM-TS), an advancement of TextNetTopics. ENTM-TS integrates multiple topic models using the Grouping, Scoring, and Modeling approach, thereby mitigating the performance variability introduced by employing individual topic modeling methods within TextNetTopics. Additionally, we performed a thorough comparative study to evaluate TextNetTopics' performance using eleven state-of-the-art topic modeling algorithms. We used the extracted topics for each as input to the G component in the TextNetTopics tool to select the most compelling topic model regarding their predictive behavior for text classification. We conducted our comprehensive evaluation utilizing the Drug-Induced Liver Injury textual dataset from the CAMDA community and the WOS-5736 dataset. The experimental results show that the Latent Semantic Indexing provides comparable performance measures with fewer discriminative features when compared with other topic modeling methods. Moreover, our evaluation reveals that the performance of ENTM-TS surpasses or aligns with the optimal outcomes obtained from individual topic models across the two datasets, establishing it as a robust and effective enhancement in text classification tasks.
