Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
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 | |
# 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. |