Added link to arXiv paper and citation, noted source dataset licenses

README.md

@@ -13,7 +13,7 @@ size_categories:
 
 <!-- Provide a quick summary of the dataset. -->
 
-This is a dataset for 3-way sentiment classification of reviews (negative, neutral, positive). It is a merge of Stanford Sentiment Treebank (SST-3) and DynaSent Rounds 1 and 2.
+This is a dataset for 3-way sentiment classification of reviews (negative, neutral, positive). It is a merge of [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) (SST-3) and [DynaSent](https://github.com/cgpotts/dynasent) Rounds 1 and 2, licensed under Apache 2.0 and Creative Commons Attribution 4.0 respectively.
 
 ## Dataset Details
 
@@ -59,7 +59,24 @@ Another potential issue is that the models will learn the dominant dataset, whic
 <!-- Provide the basic links for the dataset. -->
 
 - **Repository:** [jbeno/sentiment](https://github.com/jbeno/sentiment)
-- **Paper:** Pending
+- **Paper:** [ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis](http://arxiv.org/abs/2501.00062) (arXiv:2501.00062)
+
+## Citation
+
+If you use this material in your research, please cite:
+
+```bibtex
+@article{beno-2024-electragpt,
+      title={ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis}, 
+      author={James P. Beno},
+      journal={arXiv preprint arXiv:2501.00062},
+      year={2024},
+      eprint={2501.00062},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.00062}, 
+}
+```
 
 ## Uses