Toward Improved Clustering for Textual Data

This study explores the possibilities of combining manifold learning with contextual embedding from Transformer models for textual cluster analysis. We leverage contextual embeddings to provide a more accurate text representation for text clustering analysis and pass the embedding through a manifold learning algorithm. The results of the experiment show that manifold learning can accentuate the contextual embedding which improves the performance of the clustering algorithms in the characterization and modeling of text data. We used the resulting clusters to distinguish between relevant texts in social media campaigns and showed that the resulting embedding provides a better representation for clustering analysis.