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--- |
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language: |
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- fr |
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license: cc-by-nc-sa-4.0 |
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--- |
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> [!NOTE] |
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> Dataset origin: https://github.com/adrianchifu/FreSaDa |
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# FreSaDa: The **Fr**ench **Sa**tire **Da**ta Set |
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The FreSaDa data set contains regular and satirical samples of text collected from the French news domain. |
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## Description |
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#### General Information |
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FreSaDa, the <i>**Fre**nch **Sa**tire **Da**ta Set</i>, is composed of 11,570 articles from the newsdomain. |
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The news articles are of two types: satirical and regular. Two possible tasks may be considered on FreSaDa: |
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- Cross-domain binary classification of full news articles into *regular* versus *satirical* examples |
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- Cross-domain binary classification of headlines into *regular* versus *satirical* examples |
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The data set is divided into three subsets: |
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- training (8,716 samples) |
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- testing (2,854 samples) |
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Each sample contains the news article's title and text, as well as the corresponding label. |
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#### Data Organization |
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The data set is divided in two folders, `train` and `test`, corresponding to the two subsets for training and testing. In each folder there is a subfolder entitled `texts`, containing the texts of the news articles. Each folder also contains a file called `summary.tsv`: |
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The labels are associated as follows: |
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- 1 => Satiric News |
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- -1 => Regular News |
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If the experiments require a validation subset, the test subset may be divided into two equal parts: |
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- the samples with odd rank (1st, 3rd, 5th, ...) for validation (1,427 samples) |
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- the samples with even rank (2nd, 4th, 6th, ...) for testing (1,427 samples) |
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## Citation |
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[1] *Radu Tudor Ionescu, Adrian Gabriel Chifu.* **FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection.** In: The International Joint Conference on Neural Network, IJCNN 2021 (2021). [(link to article)](https://arxiv.org/abs/2104.04828) |
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BibTeX citation: |
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```BibTeX |
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@inproceedings{IonescuChifu2021IJCNN, |
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author = {Ionescu, Radu-Tudor and Chifu, Adrian-Gabriel}, |
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title = {FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection}, |
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year = {2021}, |
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booktitle = {The International Joint Conference on Neural Network, IJCNN 2021}, |
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series = {IJCNN2021} |
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} |
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``` |
