IEEE VIS 2024 Content: A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

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

Daniel Atzberger - University of Potsdam, Digital Engineering Faculty, Hasso Plattner Institute, Potsdam, Germany

Tim Cech - University of Potsdam, Potsdam, Germany

Willy Scheibel - Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany

Jürgen Döllner - Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany

Michael Behrisch - Utrecht University, Utrecht, Netherlands

Tobias Schreck - Graz University of Technology, Graz, Austria

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Room: Bayshore I

2024-10-17T13:06:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T13:06:00Z
Exemplar figure, described by caption below
Exemplary comparison of pairs of scatterplots. To analyze the stability concerning input data, we compare pairs of scatterplots that only differ in the amount of jitter applied to the DTM. To analyze the stability concerning hyperparameters, we compare pairs of scatterplots that differ in one hyperparameter setting with consecutive values. To analyze stability concerning randomness, we compare two layouts that only differ in their seeds.
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Keywords

Text spatializations, text embeddings, topic modeling, dimensionality reductions, stability, benchmarking

Abstract

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.