# Dataset Card for "tripadvisor-hotel-reviews"
## Dataset Description
- **Homepage:** Kaggle Challenge
- **Repository:** https://www.kaggle.com/datasets/andrewmvd/trip-advisor-hotel-reviews
- **Paper:** https://zenodo.org/record/1219899
- **Leaderboard:** N.A.
- **Point of Contact:** N.A.
### Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5.
### Languages
english 
### Citation Information
If you use this dataset in your research, please credit the authors.
Citation
Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
DOI
License
CC BY NC 4.0
Splash banner
### Contributions
Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification size_categories:10K<n<100K source_datasets:original language:en license:cc-by-nc-4.0 region:us
{"language": ["en"], "license": ["cc-by-nc-4.0"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 31840239, "num_examples": 20491}], "download_size": 19678149, "dataset_size": 31840239}}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us 

# Dataset Card for "tripadvisor-hotel-reviews"
## Dataset Description
- Homepage: Kaggle Challenge
- Repository: URL
- Paper: URL
- Leaderboard: N.A.
- Point of Contact: N.A.
### Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5.
### Languages
english 

If you use this dataset in your research, please credit the authors.
Citation
Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
DOI
License
CC BY NC 4.0
Splash banner
### Contributions
Thanks to @davidberenstein1957 for adding this dataset.
# Dataset Card for "tripadvisor-hotel-reviews" ## Dataset Description
- Homepage: Kaggle Challenge
- Repository: URL
- Paper: URL
- Leaderboard: N.A.
- Point of Contact: N.A. ### Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5. ### Languages
english 

If you use this dataset in your research, please credit the authors.
Citation
Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
DOI
License
CC BY NC 4.0
Splash banner ### Contributions
Thanks to @davidberenstein1957 for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us 
 # Dataset Card for "tripadvisor-hotel-reviews" ## Dataset Description
- Homepage: Kaggle Challenge
- Repository: URL
- Paper: URL
- Leaderboard: N.A.
- Point of Contact: N.A. ### Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5. ### Languages
english 

If you use this dataset in your research, please credit the authors.
Citation
Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
DOI
License
CC BY NC 4.0
Splash banner ### Contributions
Thanks to @davidberenstein1957 for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us 
# Dataset Card for "tripadvisor-hotel-reviews"## Dataset Description
- Homepage: Kaggle Challenge
- Repository: URL
- Paper: URL
- Leaderboard: N.A.
- Point of Contact: N.A.### Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5.### Languages
english 

If you use this dataset in your research, please credit the authors.
Citation
Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.
DOI
License
CC BY NC 4.0
Splash banner### Contributions
Thanks to @davidberenstein1957 for adding this dataset.