license: mit
configs:
- config_name: original_dataset
data_files:
- split: train
path: original_dataset/train.jsonl
- split: test
path: original_dataset/test.jsonl
- split: validation
path: original_dataset/val.jsonl
- config_name: overlapping_subset
data_files:
- split: train
path: overlapping_subset/train.jsonl
- split: test
path: overlapping_subset/test.jsonl
- split: validation
path: overlapping_subset/val.jsonl
- config_name: no_overlapping_subset
data_files:
- split: train
path: no_overlapping_subset/train.jsonl
- split: test
path: no_overlapping_subset/test.jsonl
- split: validation
path: no_overlapping_subset/val.jsonl
dataset_info:
- config_name: original_dataset
features:
- name: id_comment
dtype: string
- name: words
sequence: string
- name: triplets
list:
- name: aspect_term
sequence: string
- name: opinion_term
sequence: string
- name: aspect_position
sequence: int32
- name: opinion_position
sequence: int32
- name: polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
- name: general_polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
splits:
- name: train
num_bytes: 1115671
num_examples: 1114
- name: test
num_bytes: 239799
num_examples: 239
- name: validation
num_bytes: 237621
num_examples: 239
download_size: 2471854
dataset_size: 1593091
- config_name: no_overlapping_subset
features:
- name: id_comment
dtype: string
- name: words
sequence: string
- name: triplets
list:
- name: aspect_term
sequence: string
- name: opinion_term
sequence: string
- name: aspect_position
sequence: int32
- name: opinion_position
sequence: int32
- name: polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
- name: general_polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
splits:
- name: train
num_bytes: 270313
num_examples: 326
- name: test
num_bytes: 61779
num_examples: 70
- name: validation
num_bytes: 59399
num_examples: 71
download_size: 581415
dataset_size: 391491
- config_name: overlapping_subset
features:
- name: id_comment
dtype: string
- name: words
sequence: string
- name: triplets
list:
- name: aspect_term
sequence: string
- name: opinion_term
sequence: string
- name: aspect_position
sequence: int32
- name: opinion_position
sequence: int32
- name: polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
- name: general_polarity
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
download_size: 1890439
dataset_size: 1201600
task_categories:
- text-classification
language:
- fr
Dataset Card for TROPICAL
Table of Contents
Dataset Description
- Homepage:
- Repository: TROPICAL dataset repository
- Paper:
- Point of Contact:
Dataset Summary
The TROPICAL dataset is a French-language dataset for sentiment analysis. The dataset contains comments left by French-speaking tourists' on TripAdvisor after their visit to French Polynesia, each review either concern a hotel or a guesthouse. The format is JSON. The comments spanning from January 2001 to April 2023, the dataset contain 1592 comments along with 10729 ASTE triplets (aspect, opinion, sentiment). The unsplitted dataset is available in our Github repository.
Languages
The text in the dataset is in French as it was written by French speakers.
Dataset Structure
Data Instances
Normaly the polarity of the triplets are either "POS", "NEG" or "NEU", due to using ClassLabel the polarity is represented by 0, 1 or 2.
| String label | Int label |
|---|---|
| POS | 0 |
| NEG | 1 |
| NEU | 2 |
An example from the TROPICAL original dataset looks like the following:
{
"id_comment": "16752",
"words": ["Nous", "avons", "passé", "4", "nuits", "dans", "cet", "établissement", "Ce", "fut", "un", "très", "bon", "moment", "Le", "personnel", "très", "aimable", "et", "serviable", "Nous", "avons", "visité", "les", "plantations", "d'ananas", "en", "4/4", "et", "ce", "fut", "un", "agréable", "moment", "nous", "avons", "fait", "le", "tour", "de", "l'île", "et", "c't", "une", "splendeur", "Nous", "sommes", "revenus", "enchantés"],
"triplets": [
{"aspect_term": ["Aspect inexistant"], "opinion_term": ["revenus", "enchantés"], "aspect_position": [-1], "opinion_position": [47, 48], "polarity": "POS"},
{"aspect_term": ["tour", "de", "l'île"], "opinion_term": ["une", "splendeur"], "aspect_position": [38, 39, 40], "opinion_position": [43, 44], "polarity": "POS"},
{"aspect_term": ["moment"], "opinion_term": ["agréable"], "aspect_position": [33], "opinion_position": [32], "polarity": "POS"},
{"aspect_term": ["personnel"], "opinion_term": ["serviable"], "aspect_position": [15], "opinion_position": [19], "polarity": "POS"},
{"aspect_term": ["personnel"], "opinion_term": ["très", "aimable"], "aspect_position": [15], "opinion_position": [16, 17], "polarity": "POS"},
{"aspect_term": ["moment"], "opinion_term": ["très", "bon"], "aspect_position": [13], "opinion_position": [11, 12], "polarity": "POS"}
],
"general_polarity": "POS"
}
Data Fields
- 'id_comment': a string containing the review id
- 'words': an array of strings composing the comment
- 'triplets': a list of dictionnaries containing the following informations
- 'aspect_term': an array of strings composing the aspect term (can be a single word or a multi-word expression)
- 'opinion_term': an array of strings composing the opinion term (can be a single word or a multi-word expression)
- 'aspect_position': an array of integers indicating the position of the aspect term in the words array (can be a single integer list or a list of integers)
- 'opinion_position': an array of integers indicating the position of the opinion term in the review (can be a single integer list or a list of integers)
- 'polartiy': an integer, either 0, 1, or 2, indicating a positive, negative, or neutral sentiment, respectively
- 'general_polarity': an integer, either 0, 1, or 2, indicating a positive, negative, or neutral sentiment, respectively
Data configurations
The TROPICAL dataset has 3 configurations: original, no overlapping, and overlapping.The first one contains the 1592 comments. The overlapping dataset contains the comments that have at least one overlapping triplet. The no overlapping dataset contains the comments that have no overlapping triplet.
| Dataset Configuration | Number of comments | Number of triplets | Positive triplets | Negative triplets | Neutral triplets |
|---|---|---|---|---|---|
| original | 1,592 | 10,729 | 9,889 | 734 | 106 |
| no_overlapping | 467 | 2,235 | 2,032 | 184 | 19 |
| overlapping | 1,125 | 8,494 | 7,857 | 550 | 87 |
The following table show the splits of the dataset for all configurations:
| Dataset Configuration | Train | Test | Val |
|---|---|---|---|
| original | 1,114 | 239 | 239 |
| no_overlapping | 326 | 70 | 71 |
| overlapping | 787 | 169 | 169 |
The split values for train, test, validation are 70%, 15%, 15% respectively. The seed used is 42.
Use this dataset
from datasets import load_dataset
dataset = load_dataset("TROPICAL", "original") # or "no_overlapping" or "overlapping"
Dataset Creation
Source Data
All the comments were collected from the TripAdvisor website. The comments range from January 2001 to April 2023. The dataset contains 1592 comments along with 10729 ASTE triplets (aspect, opinion, sentiment).
Who are the source language producers?
The dataset contains tourists' comments about French Polynesia stored on the TripAdvisor website.
Known limitations
The dataset contains only comments about French Polynesia. Moreover, the dataset is not balanced, the number of positive triplets is much higher than the number of negative and neutral triplets.
Additional Information
Licensing Information
The TROPICAL dataset is licensed under the MIT License.
Citation Information
To be added...