STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module
Anton Thielmann, Arik Reuter, Christoph Weisser, Gillian Kant, Manish Kumar, Benjamin Säfken
Abstract
Topic modeling is a widely used technique to analyze large document corpora. With the ever-growing emergence of scientific contributions in the field, non-technical users may often use the simplest available software module, independent of whether there are potentially better models available. We present a Simplified Topic Retrieval, Exploration, and Analysis Module (STREAM) for user-friendly topic modelling and especially subsequent interactive topic visualization and analysis. For better topic analysis, we implement multiple intruder-word based topic evaluation metrics. Additionally, we publicize multiple new datasets that can extend the so far very limited number of publicly available benchmark datasets in topic modeling. We integrate downstream interpretable analysis modules to enable users to easily analyse the created topics in downstream tasks together with additional tabular information.The code is available at the following link: https://github.com/AnFreTh/STREAM
BibTeX
@inproceedings{thielmann-etal-2024-stream,
title = "{STREAM}: Simplified Topic Retrieval, Exploration, and Analysis Module",
author = {Thielmann, Anton and
Reuter, Arik and
Weisser, Christoph and
Kant, Gillian and
Kumar, Manish and
S{\"a}fken, Benjamin},
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-short.41/",
doi = "10.18653/v1/2024.acl-short.41",
pages = "435--444"
}