@inproceedings{029eca5d4e36475e87fc85580a631741,
title = "FuzzyTM: a Software Package for Fuzzy Topic Modeling",
abstract = "Unstructured text data is collected daily in large amounts by many organizations. Analyzing all this data is time intensive and too costly in many cases. One technique to systematically analyze large corpora of texts is topic modeling, which returns the latent topics present in a corpus. Recently, several fuzzy topic modeling algorithms have been proposed and have shown superior results over the existing algorithms. Although various Python libraries offer topic modeling algorithms, none includes fuzzy topic models. Therefore, we present FuzzyTM, a Python library for training fuzzy topic models and creating topic embeddings for downstream tasks. The user-friendly pipelines with default values allow practitioners to train a topic model with minimal effort. Meanwhile, its modular design allows researchers to modify each software element and for future methods to be added.",
keywords = "Fuzzy Clustering, Fuzzy Methods, Information Retrieval, NLP, Topic Modeling, Unsupervised Learning",
author = "Emil Rijcken and Pablo Mosteiro and Kalliopi Zervanou and Marco Spruit and Floortje Scheepers and Uzay Kaymak",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 ; Conference date: 18-07-2022 Through 23-07-2022",
year = "2022",
doi = "10.1109/FUZZ-IEEE55066.2022.9882661",
language = "English",
series = "IEEE International Conference on Fuzzy Systems",
publisher = "IEEE",
booktitle = "2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings",
address = "United States",
}