
# french_book_reviews_fr_prompt_sentiment_analysis
## Summary

**french_book_reviews_fr_prompt_sentiment_analysis** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **270,424** rows that can be used for a binary sentiment analysis task.  
The original data (without prompts) comes from the dataset [french_book_reviews](https://huggingface.co/datasets/Abirate/french_book_reviews) by Eltaief.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.


## Prompts used
### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.

```
'Commentaire : "'+review+'" Le commentaire est-il positif ou négatif ?',  
"""Avis : " """+review+""" " L'avis est-il positif ou négatif ?""",  
'Critique : "'+review+'" La critique est-elle positive ou négative ?',  
"""Evaluation : " """+review+""" " L'évaluation est-elle positive ou négative ?""",  
'Ce commentaire sur le produit est-il positif ou négatif ? \nCommentaire : "'+review+'"\nRéponse :',  
'Cet avis sur le produit est-il positif ou négatif ? \nAvis : "'+review+'"\nRéponse :',  
'Cette critique sur le produit est-elle positive ou négative ? \nCritique : "'+review+'"\nRéponse :',  
'Cette évaluation sur le produit est-elle positive ou négative ? \nEvaluation : "'+review+'"\nRéponse :',  
'Commentaire : "'+review+'"\n Ce commentaire sur le produit exprime-t-il un sentiment négatif ou positif ?',  
'Avis : "'+review+'"\n Cet avis sur le produit exprime-t-il un sentiment négatif ou positif ?',  
'Critique : "'+review+'"\n Cette critique sur le produit exprime-t-il un sentiment négatif ou positif ?',  
'Evaluation : "'+review+'"\n Cette évaluation sur le produit exprime-t-il un sentiment négatif ou positif ?',  
'Ce commentaire sur le produit a-t-il un ton négatif ou positif ? \n Commentaire : "'+review+'"\n Réponse :',   
'Cet avis sur le produit a-t-il un ton négatif ou positif ? \n Avis : "'+review+'"\n Réponse :',   
'Cette critique sur le produit a-t-il un ton négatif ou positif ? \n Evaluation : "'+review+'"\n Réponse :',   
'Cette évaluation sur le produit a-t-il un ton négatif ou positif ? \n Avis : "'+review+'"\n Réponse :',   
"""Voici un commentaire laissé par un client sur un produit. Diriez-vous qu'il est négatif ou positif ? \nCommentaire : """+review,  
"""Voici un avis laissé par un client sur un produit. Diriez-vous qu'il est négatif ou positif ? \nAvis : """+review,  
"""Voici une critique laissée par un client sur un produit. Diriez-vous qu'elle est négative ou positive ? \nCritique : """+review,  
"""Voici une évaluation laissée par un client sur un produit. Diriez-vous qu'elle est négative ou positive ? \nEvaluation : """+review,  
'Commentaire du produit : "'+review+'" Ce commentaire dépeint le produit sous un angle négatif ou positif ?',  
'Avis du produit : "'+review+'" Cet avis dépeint le produit sous un angle négatif ou positif ?',  
'Critique du produit : "'+review+'" Cette critique dépeint le produit sous un angle négatif ou positif ?',  
'Evaluation du produit : "'+review+'" Cette évaluation dépeint le produit sous un angle négatif ou positif ?',  
'Le commentaire suivant exprime quel sentiment ?\n Commentaire' +review,  
"""L'avis suivant exprime quel sentiment ?\n Avis""" +review,  
'La critique suivante exprime quel sentiment ?\n Critique' +review,  
"""L'évaluation suivante exprime quel sentiment ?\n Evaluation""" +review
```

### Features used in the prompts
In the prompt list above, `review` and `targets` have been constructed from:
```
fbr = load_dataset('Abirate/french_book_reviews')
review = fbr['train']['reader_review'][i]
if fbr['train']['rating'][i] < 2.5:
   targets.append("neg")
else :
   targets.append("pos")
```



# Splits
- `train` with 270,424 samples
- no `valid` split
- no `test` split
  

# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/french_book_reviews_fr_prompt_sentiment_analysis")
```

# Citation
## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { https://huggingface.co/datasets/Abirate/french_book_reviews },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }}


## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { https://huggingface.co/datasets/CATIE-AQ/DFP },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
}



## License
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
task_categories:text-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:french_book_reviews language:fr license:cc binary-sentiment-analysis DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["french_book_reviews"], "task_categories": ["text-classification"], "tags": ["binary-sentiment-analysis", "DFP", "french prompts"]}
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-french_book_reviews #language-French #license-cc #binary-sentiment-analysis #DFP #french prompts #region-us 


# french_book_reviews_fr_prompt_sentiment_analysis
## Summary

french_book_reviews_fr_prompt_sentiment_analysis is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a binary sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al.


## Prompts used
### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.



### Features used in the prompts
In the prompt list above, 'review' and 'targets' have been constructed from:




# Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split
  

# How to use?


## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }}


## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
}



## License
CC0: Public Domain
# french_book_reviews_fr_prompt_sentiment_analysis ## Summary

french_book_reviews_fr_prompt_sentiment_analysis is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a binary sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al. ## Prompts used ### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ### Features used in the prompts
In the prompt list above, 'review' and 'targets' have been constructed from: # Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }} ## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
} ## License
CC0: Public Domain
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-french_book_reviews #language-French #license-cc #binary-sentiment-analysis #DFP #french prompts #region-us 
 # french_book_reviews_fr_prompt_sentiment_analysis ## Summary

french_book_reviews_fr_prompt_sentiment_analysis is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a binary sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al. ## Prompts used ### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ### Features used in the prompts
In the prompt list above, 'review' and 'targets' have been constructed from: # Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }} ## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
} ## License
CC0: Public Domain
passage: TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-french_book_reviews #language-French #license-cc #binary-sentiment-analysis #DFP #french prompts #region-us 
# french_book_reviews_fr_prompt_sentiment_analysis## Summary

french_book_reviews_fr_prompt_sentiment_analysis is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a binary sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al.## Prompts used### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.### Features used in the prompts
In the prompt list above, 'review' and 'targets' have been constructed from:# Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split# How to use?## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }}