
# Dataset Card for MultiBooked

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** http://hdl.handle.net/10230/33928
- **Repository:** https://github.com/jerbarnes/multibooked
- **Paper:** https://arxiv.org/abs/1803.08614
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

- `text`: layer of the original text.
  - `wid`: list of word IDs for each word within the example.
  - `sent`: list of sentence IDs for each sentence within the example.
  - `para`: list of paragraph IDs for each paragraph within the example.
  - `word`: list of words.
- `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
  PoS tagging)
  - `tid`: list of term IDs for each term within the example.
  - `lemma`: list of lemmas.
  - `morphofeat`: list of morphological features.
  - `pos`: list of PoS tags.
  - `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
    in a one-to-one correspondence between words and terms).
- `opinions`: layer of the opinions in the text.
  - `oid`: list of opinion IDs
  - `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder.
  - `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target.
  - `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values:
    `StrongNegative`, `Negative`, `Positive`, or `StrongPositive`.
  - `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression.

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

Dataset is under the [CC-BY 3.0](https://creativecommons.org/licenses/by/3.0/) license.

### Citation Information

```
@inproceedings{Barnes2018multibooked,
    author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
    title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
    year = {2018},
    month = {May},
    date = {7-12},
    address = {Miyazaki, Japan},
    publisher = {European Language Resources Association (ELRA)},
    language = {english}
}
```

### Contributions

Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:n<1K source_datasets:original language:ca language:eu license:cc-by-3.0 arxiv:1803.08614 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca", "eu"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "multibooked", "pretty_name": "MultiBooked", "config_names": ["ca", "eu"], "dataset_info": [{"config_name": "ca", "features": [{"name": "text", "sequence": [{"name": "wid", "dtype": "string"}, {"name": "sent", "dtype": "string"}, {"name": "para", "dtype": "string"}, {"name": "word", "dtype": "string"}]}, {"name": "terms", "sequence": [{"name": "tid", "dtype": "string"}, {"name": "lemma", "dtype": "string"}, {"name": "morphofeat", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "target", "sequence": "string"}]}, {"name": "opinions", "sequence": [{"name": "oid", "dtype": "string"}, {"name": "opinion_holder_target", "sequence": "string"}, {"name": "opinion_target_target", "sequence": "string"}, {"name": "opinion_expression_polarity", "dtype": {"class_label": {"names": {"0": "StrongNegative", "1": "Negative", "2": "Positive", "3": "StrongPositive"}}}}, {"name": "opinion_expression_target", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 1952731, "num_examples": 567}], "download_size": 4429415, "dataset_size": 1952731}, {"config_name": "eu", "features": [{"name": "text", "sequence": [{"name": "wid", "dtype": "string"}, {"name": "sent", "dtype": "string"}, {"name": "para", "dtype": "string"}, {"name": "word", "dtype": "string"}]}, {"name": "terms", "sequence": [{"name": "tid", "dtype": "string"}, {"name": "lemma", "dtype": "string"}, {"name": "morphofeat", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "target", "sequence": "string"}]}, {"name": "opinions", "sequence": [{"name": "oid", "dtype": "string"}, {"name": "opinion_holder_target", "sequence": "string"}, {"name": "opinion_target_target", "sequence": "string"}, {"name": "opinion_expression_polarity", "dtype": {"class_label": {"names": {"0": "StrongNegative", "1": "Negative", "2": "Positive", "3": "StrongPositive"}}}}, {"name": "opinion_expression_target", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 1175816, "num_examples": 343}], "download_size": 4429415, "dataset_size": 1175816}]}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Catalan #language-Basque #license-cc-by-3.0 #arxiv-1803.08614 #region-us 


# Dataset Card for MultiBooked

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard:
- Point of Contact:

### Dataset Summary

MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from URL. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.

### Supported Tasks and Leaderboards



### Languages

Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque

## Dataset Structure

### Data Instances



### Data Fields

- 'text': layer of the original text.
  - 'wid': list of word IDs for each word within the example.
  - 'sent': list of sentence IDs for each sentence within the example.
  - 'para': list of paragraph IDs for each paragraph within the example.
  - 'word': list of words.
- 'terms': layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
  PoS tagging)
  - 'tid': list of term IDs for each term within the example.
  - 'lemma': list of lemmas.
  - 'morphofeat': list of morphological features.
  - 'pos': list of PoS tags.
  - 'target': list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
    in a one-to-one correspondence between words and terms).
- 'opinions': layer of the opinions in the text.
  - 'oid': list of opinion IDs
  - 'opinion_holder_target': list of sublists of the corresponding term IDs that span the opinion holder.
  - 'opinion_target_target': list of sublists of the corresponding term IDs that span the opinion target.
  - 'opinion_expression_polarity': list of the opinion expression polarities. The polarity can take one of the values:
    'StrongNegative', 'Negative', 'Positive', or 'StrongPositive'.
  - 'opinion_expression_target': list of sublists of the corresponding term IDs that span the opinion expression.

### Data Splits



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information

Dataset is under the CC-BY 3.0 license.





### Contributions

Thanks to @albertvillanova for adding this dataset.
# Dataset Card for MultiBooked ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard:
- Point of Contact: ### Dataset Summary

MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from URL. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project. ### Supported Tasks and Leaderboards ### Languages

Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque ## Dataset Structure ### Data Instances ### Data Fields

- 'text': layer of the original text.
  - 'wid': list of word IDs for each word within the example.
  - 'sent': list of sentence IDs for each sentence within the example.
  - 'para': list of paragraph IDs for each paragraph within the example.
  - 'word': list of words.
- 'terms': layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
  PoS tagging)
  - 'tid': list of term IDs for each term within the example.
  - 'lemma': list of lemmas.
  - 'morphofeat': list of morphological features.
  - 'pos': list of PoS tags.
  - 'target': list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
    in a one-to-one correspondence between words and terms).
- 'opinions': layer of the opinions in the text.
  - 'oid': list of opinion IDs
  - 'opinion_holder_target': list of sublists of the corresponding term IDs that span the opinion holder.
  - 'opinion_target_target': list of sublists of the corresponding term IDs that span the opinion target.
  - 'opinion_expression_polarity': list of the opinion expression polarities. The polarity can take one of the values:
    'StrongNegative', 'Negative', 'Positive', or 'StrongPositive'.
  - 'opinion_expression_target': list of sublists of the corresponding term IDs that span the opinion expression. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

Dataset is under the CC-BY 3.0 license. ### Contributions

Thanks to @albertvillanova for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Catalan #language-Basque #license-cc-by-3.0 #arxiv-1803.08614 #region-us 
 # Dataset Card for MultiBooked ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard:
- Point of Contact: ### Dataset Summary

MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from URL. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project. ### Supported Tasks and Leaderboards ### Languages

Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque ## Dataset Structure ### Data Instances ### Data Fields

- 'text': layer of the original text.
  - 'wid': list of word IDs for each word within the example.
  - 'sent': list of sentence IDs for each sentence within the example.
  - 'para': list of paragraph IDs for each paragraph within the example.
  - 'word': list of words.
- 'terms': layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
  PoS tagging)
  - 'tid': list of term IDs for each term within the example.
  - 'lemma': list of lemmas.
  - 'morphofeat': list of morphological features.
  - 'pos': list of PoS tags.
  - 'target': list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
    in a one-to-one correspondence between words and terms).
- 'opinions': layer of the opinions in the text.
  - 'oid': list of opinion IDs
  - 'opinion_holder_target': list of sublists of the corresponding term IDs that span the opinion holder.
  - 'opinion_target_target': list of sublists of the corresponding term IDs that span the opinion target.
  - 'opinion_expression_polarity': list of the opinion expression polarities. The polarity can take one of the values:
    'StrongNegative', 'Negative', 'Positive', or 'StrongPositive'.
  - 'opinion_expression_target': list of sublists of the corresponding term IDs that span the opinion expression. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

Dataset is under the CC-BY 3.0 license. ### Contributions

Thanks to @albertvillanova for adding this dataset.