Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+
+
+## Sentiment Analysis Data for the Slovak Language
+
+**Dataset Description:**
+This dataset contains a sentiment analysis dataset from Pecar et al. (2019).
+
+**Data Structure:**
+The data was used for the project on [improving word embeddings with graph knowledge for Low Resource Languages](https://github.com/pyRis/retrofitting-embeddings-lrls?tab=readme-ov-file).
+
+**Citation:**
+```bibtex
+@inproceedings{pecar-etal-2019-improving,
+    title = "Improving Sentiment Classification in {S}lovak Language",
+    author = "Pecar, Samuel  and
+      Simko, Marian  and
+      Bielikova, Maria",
+    editor = "Erjavec, Toma{\v{z}}  and
+      Marci{\'n}czuk, Micha{\l}  and
+      Nakov, Preslav  and
+      Piskorski, Jakub  and
+      Pivovarova, Lidia  and
+      {\v{S}}najder, Jan  and
+      Steinberger, Josef  and
+      Yangarber, Roman",
+    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
+    month = aug,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-3716",
+    doi = "10.18653/v1/W19-3716",
+    pages = "114--119",
+    abstract = "Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.",
+}
+```