Add arXiv paper

BERTopic: Neural topic modeling with a class-based TF-IDF procedure

README.md

@@ -3,7 +3,8 @@
 [![docs](https://img.shields.io/badge/docs-Passing-green.svg)](https://maartengr.github.io/BERTopic/)
 [![PyPI - PyPi](https://img.shields.io/pypi/v/BERTopic)](https://pypi.org/project/bertopic/)
 [![PyPI - License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/MaartenGr/VLAC/blob/master/LICENSE)
-[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.4381785-blue)](https://doi.org/10.5281/zenodo.4381785)
+[![arXiv](https://img.shields.io/badge/arXiv-2203.05794-<COLOR>.svg)](https://arxiv.org/abs/2203.05794)
+
 
 # BERTopic
 
@@ -18,7 +19,7 @@ BERTopic supports
 and [**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html) topic modeling. It even supports visualizations similar to LDAvis!
 
 Corresponding medium posts can be found [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) 
-and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4).
+and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4). For a more detailed overview, you can read the [paper](https://arxiv.org/abs/2203.05794). 
 
 ## Installation