ML Anthology

Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering

Nicolas Médoc, Mohammad Ghoniem, Mohamed Nadif
ECML-PKDD 2016 pp. 59-62
doi:10.1007/978-3-319-46131-1_13 /ecmlpkdd/2016/medoc2016ecmlpkdd-exploratory/

Abstract

We propose a visual analytics tool to support analytic journalists in the exploration of large text corpora. Our tool combines graph modularity-based diagonal biclustering to extract high-level topics with overlapping bi-clustering to elicit fine-grained topic variants. A hybrid topic treemap visualization gives the analyst an overview of all topics. Coordinated sunburst and heatmap visualizations let the analyst inspect and compare topic variants and access document content on demand.

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Cite

Text

Médoc et al. "Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_13

Markdown

[Médoc et al. "Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/medoc2016ecmlpkdd-exploratory/) doi:10.1007/978-3-319-46131-1_13

BibTeX

@inproceedings{medoc2016ecmlpkdd-exploratory,
  title     = {{Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering}},
  author    = {Médoc, Nicolas and Ghoniem, Mohammad and Nadif, Mohamed},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2016},
  pages     = {59-62},
  doi       = {10.1007/978-3-319-46131-1_13},
  url       = {https://mlanthology.org/ecmlpkdd/2016/medoc2016ecmlpkdd-exploratory/}
}