Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
sentiment-analysis
Languages:
Italian
Size:
1K - 10K
License:
| language: | |
| - it | |
| language_details: it-IT | |
| license: cc-by-nc-sa-4.0 | |
| task_ids: | |
| - sentiment-analysis | |
| task_categories: | |
| - text-classification | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: train.jsonl | |
| - split: test | |
| path: test.jsonl | |
| size_categories: | |
| - 1K<n<10K | |
| SENTIPOLC 2016 dataset | |
| The SENTIPOLC 2016 dataset contains 9410 tweets annotated for subjectivity, overall and literal polarity, and irony. | |
| The dataset has been created and used in the context of the SENTIPOLC 2016 task (http://www.di.unito.it/~tutreeb/sentipolc-evalita16/index.html), organized as part of the EVALITA 2016 evaluation campaign. | |
| Original files available here: | |
| https://live.european-language-grid.eu/catalogue/corpus/7479/download/ | |
| If you find this dataset useful please cite: | |
| ``` | |
| @inproceedings{barbieri2016overview, | |
| title={Overview of the evalita 2016 sentiment polarity classification task}, | |
| author={Barbieri, Francesco and Basile, Valerio and Croce, Danilo and Nissim, Malvina and Novielli, Nicole and Patti, Viviana and others}, | |
| booktitle={CEUR Workshop Proceedings}, | |
| volume={1749}, | |
| year={2016}, | |
| organization={CEUR-WS} | |
| } | |
| ``` |