
## Dataset overview
This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. 
The dataset is formatted using the 🤗[DatasetDict](https://huggingface.co/docs/datasets/index) format and contains the following indices:
- train, 116693 records
- test, 14587 records
- validation, 14587 records

The dataset holds both information of the restaurant level as well as the review level and contains the following features:
- [restaurant_ID] > unique restaurant ID
- [restaurant_review_ID] > unique review ID
- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020
- [score_total] > restaurant level total score
- [score_food] > restaurant level food score
- [score_service] > restaurant level service score
- [score_decor] > restaurant level decor score
- [fame_reviewer] > label for how often a reviewer has posted a restaurant review
- [reviewscore_food] > review level food score
- [reviewscore_service] > review level service score
- [reviewscore_ambiance] > review level ambiance score
- [reviewscore_waiting] > review level waiting score
- [reviewscore_value] > review level value for money score
- [reviewscore_noise] > review level noise score
- [review_text] > the full review that was written by the reviewer for this restaurant
- [review_length] > total length of the review (tokens)

## Purpose
The restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In [this blog series](https://medium.com/broadhorizon-cmotions/natural-language-processing-for-predictive-purposes-with-r-cb65f009c12b) we used the review texts to predict next Michelin star restaurants, using R. 
language:nl text-classification sentiment-analysis region:us
{"language": ["nl"], "tags": ["text-classification", "sentiment-analysis"], "datasets": ["train", "test", "validation"]}
TAGS
#language-Dutch #text-classification #sentiment-analysis #region-us 


## Dataset overview
This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. 
The dataset is formatted using the DatasetDict format and contains the following indices:
- train, 116693 records
- test, 14587 records
- validation, 14587 records

The dataset holds both information of the restaurant level as well as the review level and contains the following features:
- [restaurant_ID] > unique restaurant ID
- [restaurant_review_ID] > unique review ID
- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020
- [score_total] > restaurant level total score
- [score_food] > restaurant level food score
- [score_service] > restaurant level service score
- [score_decor] > restaurant level decor score
- [fame_reviewer] > label for how often a reviewer has posted a restaurant review
- [reviewscore_food] > review level food score
- [reviewscore_service] > review level service score
- [reviewscore_ambiance] > review level ambiance score
- [reviewscore_waiting] > review level waiting score
- [reviewscore_value] > review level value for money score
- [reviewscore_noise] > review level noise score
- [review_text] > the full review that was written by the reviewer for this restaurant
- [review_length] > total length of the review (tokens)

## Purpose
The restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R. 
## Dataset overview
This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. 
The dataset is formatted using the DatasetDict format and contains the following indices:
- train, 116693 records
- test, 14587 records
- validation, 14587 records

The dataset holds both information of the restaurant level as well as the review level and contains the following features:
- [restaurant_ID] > unique restaurant ID
- [restaurant_review_ID] > unique review ID
- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020
- [score_total] > restaurant level total score
- [score_food] > restaurant level food score
- [score_service] > restaurant level service score
- [score_decor] > restaurant level decor score
- [fame_reviewer] > label for how often a reviewer has posted a restaurant review
- [reviewscore_food] > review level food score
- [reviewscore_service] > review level service score
- [reviewscore_ambiance] > review level ambiance score
- [reviewscore_waiting] > review level waiting score
- [reviewscore_value] > review level value for money score
- [reviewscore_noise] > review level noise score
- [review_text] > the full review that was written by the reviewer for this restaurant
- [review_length] > total length of the review (tokens) ## Purpose
The restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R.
TAGS
#language-Dutch #text-classification #sentiment-analysis #region-us 
 ## Dataset overview
This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. 
The dataset is formatted using the DatasetDict format and contains the following indices:
- train, 116693 records
- test, 14587 records
- validation, 14587 records

The dataset holds both information of the restaurant level as well as the review level and contains the following features:
- [restaurant_ID] > unique restaurant ID
- [restaurant_review_ID] > unique review ID
- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020
- [score_total] > restaurant level total score
- [score_food] > restaurant level food score
- [score_service] > restaurant level service score
- [score_decor] > restaurant level decor score
- [fame_reviewer] > label for how often a reviewer has posted a restaurant review
- [reviewscore_food] > review level food score
- [reviewscore_service] > review level service score
- [reviewscore_ambiance] > review level ambiance score
- [reviewscore_waiting] > review level waiting score
- [reviewscore_value] > review level value for money score
- [reviewscore_noise] > review level noise score
- [review_text] > the full review that was written by the reviewer for this restaurant
- [review_length] > total length of the review (tokens) ## Purpose
The restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R.
passage: TAGS
#language-Dutch #text-classification #sentiment-analysis #region-us 
## Dataset overview
This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. 
The dataset is formatted using the DatasetDict format and contains the following indices:
- train, 116693 records
- test, 14587 records
- validation, 14587 records

The dataset holds both information of the restaurant level as well as the review level and contains the following features:
- [restaurant_ID] > unique restaurant ID
- [restaurant_review_ID] > unique review ID
- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020
- [score_total] > restaurant level total score
- [score_food] > restaurant level food score
- [score_service] > restaurant level service score
- [score_decor] > restaurant level decor score
- [fame_reviewer] > label for how often a reviewer has posted a restaurant review
- [reviewscore_food] > review level food score
- [reviewscore_service] > review level service score
- [reviewscore_ambiance] > review level ambiance score
- [reviewscore_waiting] > review level waiting score
- [reviewscore_value] > review level value for money score
- [reviewscore_noise] > review level noise score
- [review_text] > the full review that was written by the reviewer for this restaurant
- [review_length] > total length of the review (tokens)## Purpose
The restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R.