Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TwitterSent
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
Dataset Card for DKHate
Table of Contents
Dataset Description
- Repository: https://github.com/fnielsen/lcc-sentiment
- Direct Download part 1: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_mixed_2014_10K-sentences.csv
- Direct Download part 2: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_newscrawl_2011_10K-sentences.csv
Dataset Summary
This dataset consists of Danish data from the Leipzig Collection that has been annotated for sentiment analysis by Finn Årup Nielsen.
Supported Tasks and Leaderboards
This dataset is suitable for sentiment analysis.
Languages
This dataset is in Danish.
Dataset Structure
Data Instances
Every entry in the dataset has a document and an associated label.
Data Fields
An entry in the dataset consists of the following fields:
text
(str
): The text content.label
(str
): The label of thetext
. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
Data Splits
A train
and test
split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 349 documents in the training split and 150 in the test split.
Additional Information
Dataset Curators
The collection and annotation of the dataset is solely due to the Finn Årup Nielsen. It was originally annotated as a score between -5 and +5, but the labels in this version have been converted to a negative, neutral and positive label.
Licensing Information
The dataset is released under the CC BY 4.0 license.
Citation Information
@misc{lcc,
title={LCC},
author={Finn Årup Nielsen},
year={2016},
note={\url{https://github.com/fnielsen/lcc-sentiment}}
}
Contributions
Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.