README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
language:
- hau
- ibo
- pcm
- yor
pretty_name: NaijaSenti
---

<p align="center">
<img src="https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/image/naijasenti_logo1.png", width="500">

--------------------------------------------------------------------------------

## Dataset Description


- **Homepage:** https://github.com/hausanlp/NaijaSenti
- **Repository:** [GitHub](https://github.com/hausanlp/NaijaSenti)
- **Paper:**  [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://aclanthology.org/2022.lrec-1.63/)
- **Leaderboard:** N/A
- **Point of Contact:** [Shamsuddeen Hassan Muhammad](shamsuddeen2004@gmail.com)


### Dataset Summary

 NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.


### Supported Tasks and Leaderboards

The NaijaSenti can be used for a wide range of sentiment analysis tasks in Nigerian languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages. It was part of the datasets that were used for [SemEval 2023 Task 12: Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320).


### Languages

4 most spoken Nigerian languages 

* Hausa (hau) 
* Igbo (ibo)
* Nigerian Pidgin (pcm)
* Yoruba (yor) 


## Dataset Structure

### Data Instances

For each instance, there is a string for the tweet and a string for the label. See the NaijaSenti [dataset viewer](https://huggingface.co/datasets/HausaNLP/NaijaSenti-Twitter/viewer/hau/train) to explore more examples.


```
{
  "tweet": "string",
  "label": "string"
}
```


### Data Fields

The data fields are:

```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```


### Data Splits

The NaijaSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.

|  | hau | ibo | pcm | yor |
|---|---|---|---|---|
| train | 14,172 | 10,192 | 5,121 | 8,522  |
| dev | 2,677 | 1,841 | 1,281 | 2,090 |
| test | 5,303 | 3,682 | 4,154 | 4,515 |
| total | 22,152 | 15,715 | 10,556 | 15,127 |


### How to use it


```python
from  datasets  import  load_dataset

# you can load specific languages (e.g., Hausa). This download train, validation and test sets. 
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau")

# train set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "train")

# test set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "test")

# validation set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "validation")

```



## Dataset Creation

### Curation Rationale

NaijaSenti Version 1.0.0 aimed to be used sentiment analysis and other related task in Nigerian indigenous and creole languages - Hausa, Igbo, Nigerian Pidgin and Yoruba.


### Source Data

Twitter


### Personal and Sensitive Information

We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.


## Considerations for Using the Data

### Social Impact of Dataset

The NaijaSenti dataset has the potential to improve sentiment analysis for Nigerian languages, which is essential for understanding and analyzing the diverse perspectives of people in Nigeria. This dataset can enable researchers and developers to create sentiment analysis models that are specific to Nigerian languages, which can be used to gain insights into the social, cultural, and political views of people in Nigerian. Furthermore, this dataset can help address the issue of underrepresentation of Nigerian languages in natural language processing, paving the way for more equitable and inclusive AI technologies.


## Additional Information

### Dataset Curators

* Shamsuddeen Hassan Muhammad
* Idris Abdulmumin
* Ibrahim Said Ahmad
* Bello Shehu Bello


### Licensing Information

This NaijaSenti is licensed under a Creative Commons Attribution BY-NC-SA 4.0 International License




### Citation Information

```
@inproceedings{muhammad-etal-2022-naijasenti,
    title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
    author = "Muhammad, Shamsuddeen Hassan  and
      Adelani, David Ifeoluwa  and
      Ruder, Sebastian  and
      Ahmad, Ibrahim Sa{'}id  and
      Abdulmumin, Idris  and
      Bello, Bello Shehu  and
      Choudhury, Monojit  and
      Emezue, Chris Chinenye  and
      Abdullahi, Saheed Salahudeen  and
      Aremu, Anuoluwapo  and
      Jorge, Al{\'\i}pio  and
      Brazdil, Pavel",
    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-1.63",
    pages = "590--602",
}
```


### Contributions

> This work was carried out with support from Lacuna Fund, an initiative co-founded by The Rockefeller Foundation, Google.org, and Canada’s International Development Research Centre. The views expressed herein do not necessarily represent those of Lacuna Fund, its Steering Committee, its funders, or Meridian Institute.