README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
languages:
  - ca
  - eu
licenses:
  - 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
configs:
  - ca
  - eu

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: 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 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 for adding this dataset.