Published November 28, 2025
| Version v1
xSPAM: Introducing and Evaluating Explainability for a Supervised Topic Model
Description
Supplementary material for paper "xSPAM: Introducing and Evaluating Explainability for a Supervised Topic Model"
1_xSPAM_Dataset_20newsgroups.ipynb # Code for the xSPAM model for the 20 newsgroup dataset.
1_xSPAM_Dataset_arxiv.ipynb # Code for the xSPAM model for the Arxiv dataset.
2_Dataset_20newsgroups-sLDA.ipynb # Code for the sLDA model for the 20 newsgroup dataset.
2_Dataset_arxiv-own-SLDA.ipynb # Code for the sLDA model for the Arxiv dataset.
3_Dataset_20newsgroups-bertopic.ipynb # Code for the bertopicmodel for the 20 newsgroup dataset.
3_Dataset_arxiv-bertopic.ipynb # Code for the bertopicmodel for the Arxiv dataset.
tomotopy_pam.zip # Compiled python extension together with python. Can be placed inside the site-packages folder in your python environment.
src.zip # Source code for the python extension
xSPAM user-study questionair.pdf # The Questionair that the participants got.
xSPAM user-study answers.pdf # The answers received from participants, excluding the open questions for anonymity reasons.
Files
1_xSPAM_Dataset_20newsgroups.ipynb
Files
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