A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

• Main Authors: Atzberger, Daniel, Cech, Tim, Scheibel, Willy, Döllner, Jürgen, Behrisch, Michael, Schreck, Tobias
• Format: Journal Article
• Language: English
• Published: 25-07-2024
• Subjects: Computer Science - Computation and Language; Computer Science - Learning
• DOI: 10.48550/arxiv.2407.17876

The semantic similarity between documents of a text corpus can be visualized
using map-like metaphors based on two-dimensional 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
42817 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
https://github.com/hpicgs/Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study
and results as Zenodo archive at https://doi.org/10.5281/zenodo.12772898.