TY - JOUR
T1 - A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
AU - Atzberger, Daniel
AU - Cech, Tim
AU - Scheibel, Willy
AU - Dollner, Jurgen
AU - Behrisch, Michael
AU - Schreck, Tobias
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2025/1
Y1 - 2025/1
N2 - The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on twodimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-DimensionalityReduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.
AB - The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on twodimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-DimensionalityReduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.
KW - benchmarking
KW - dimensionality reductions
KW - stability
KW - text embeddings
KW - Text spatializations
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85204620943&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2024.3456308
DO - 10.1109/TVCG.2024.3456308
M3 - Article
AN - SCOPUS:85204620943
SN - 1077-2626
VL - 31
SP - 305
EP - 315
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
ER -