Datasets:
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---
language:
- en
- ar
- fr
- de
- hi
- it
- pt
- es
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-tweet-datasets
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tweet_sentiment_multilingual
pretty_name: Tweet Sentiment Multilingual
train-eval-index:
- config: sentiment
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
configs:
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
dataset_info:
- config_name: sentiment
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
0: negative
1: neutral
2: positive
---
# Dataset Card for cardiffnlp/tweet_sentiment_multilingual
## Dataset Description
- **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t)
- **Repository:** - **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t)
- **Paper:** [https://aclanthology.org/2022.lrec-1.27/](https://aclanthology.org/2022.lrec-1.27/)
- **Point of Contact:** [Asahi Ushio](https://asahiushio.com/)
### Dataset Summary
Tweet Sentiment Multilingual consists of sentiment analysis dataset on Twitter in 8 different lagnuages.
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
### Supported Tasks and Leaderboards
- `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.
## Dataset Structure
### Data Instances
An instance from `sentiment` config:
```
{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}
```
### Data Fields
For `sentiment` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: negative
`1`: neutral
`2`: positive
### Data Splits
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
| name | train | validation | test |
| --------------- | ----- | ---------- | ----- |
| arabic | 1838 | 323 | 869 |
| english | 1838 | 323 | 869 |
| french | 1838 | 323 | 869 |
| german | 1838 | 323 | 869 |
| hindi | 1838 | 323 | 869 |
| italian | 1838 | 323 | 869 |
| portuguese | 1838 | 323 | 869 |
| spanish | 1838 | 323 | 869 |
### Dataset Curators
Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.
### Licensing Information
[Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ), and all of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy)
### Citation Information
```
@inproceedings{barbieri-etal-2022-xlm,
title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
author = "Barbieri, Francesco and
Espinosa Anke, Luis and
Camacho-Collados, Jose",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.27",
pages = "258--266",
abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.",
}
```
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