Update README.md

README.md

@@ -1,3 +1,32 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- gn
+- es
+pretty_name: JOTAD
+size_categories:
+- 1K<n<10K
+---
+# Text-based afective computing
+We collected a dataset of tweets primarily written in Guarani (and Jopara, a code-switching language that combines Guarani and Spanish) and annotated them for three widely-used dimensions in sentiment analysis: 
+1. emotion recognition (https://huggingface.co/datasets/mmaguero/gn-emotion-recognition),  
+2. humor detection (**this repo**, https://huggingface.co/datasets/mmaguero/gn-humor-detection), and
+3. ofensive language identifcation (https://huggingface.co/datasets/mmaguero/gn-offensive-language-identifcation).
+
+The statistics for the Jopara afective analysis datasets and their splits for each proposed task:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6193f446979eb76ec3cc5b89/vKw1k6v4ZGtsj8b1BgPYM.png)
+
+## How cite?
+
+```
+@article{aguero-et-al2023multi-affect-low-langs-grn,
+  title={Multidimensional Affective Analysis for Low-resource Languages: A Use Case with Guarani-Spanish Code-switching Language},
+  author={Agüero-Torales, Marvin Matías, López-Herrera, Antonio Gabriel, and Vilares, David},
+  journal={Cognitive Computation},
+  year={2023},
+  publisher={Springer},
+  notes={https://link.springer.com/article/10.1007/s12559-023-10165-0#citeas}
+}
+```