--- 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 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 config_names: - ca - eu --- # 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.