NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages

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Deep Learning NaijaSenti

Overview

NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages. This project was supported by lacuna-fund initiatives. Jump straight to one of the sections below, or just scroll down to find out more.

Paper
Abstract
Language Resource Developed
papers from this project
Contact us

Paper

Read the NaijaSenti paper here:

Abstract

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce 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 (except for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing, and labelling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We make the datasets, trained models, sentiment lexicons, and code available to encourage sentiment analysis research in under-represented languages.

Download NaijaSenti Datasets

1. Manually Annotated Twitter Sentiment Dataset

2. Manually Annotated Sentiment Lexicon

3. Semi-automatically Translated emotion lexicon

4. Semi-automatically Translated sentiment lexicon

5. Large Scale Unlabled Twitter Sentiment Corpus

5. Stop-words for Hausa, Igbo, Pidgin and Yoruba

Model

Citation

If you use this data in your work, please cite:

@misc{muhammad2022naijasenti,
      title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis}, 
      author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris Chinenye Emezue and Anuoluwapo Aremu and Saheed Abdul and Pavel Brazdil},
      year={2022},
      eprint={2201.08277},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Papers from this project

Please, let us know if you use NaijaSenti in your papers:

Contact us

If you want to report a problem or suggest an enhancement we'd love for you to open an issue at this github repository because then we can get right on it. But you can also contact us by email (hausanlp AT gmail DOT com) or on twitter.

Changelog

2022-01-21: Released NaijaSenti v1.0.0

License

The dataset is licenced under CC-BY-SA, see the LICENSE file for details.

Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档队名：W03KFgNOc 排名：3 正确率: 0.75564 队员：yyMoming,xkwang,RichardoMu。比赛链接：人脸情绪识别挑战赛文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022

Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Emotion and Theme Recognition in Music The repository contains code for the submission of the lileonardo team to the 2021 Emotion and Theme Recognitio

8 Aug 2, 2022

Face Recognition and Emotion Detector Device

Face Recognition and Emotion Detector Device Orange PI 1 Python 3.10.0 + Django 3.2.9 Project's file explanation Django manage.py Django commands hand

2 Dec 21, 2021

Official repository of the AAAI'2022 paper "Contrast and Generation Make BART a Good Dialogue Emotion Recognizer"

CoG-BART Contrast and Generation Make BART a Good Dialogue Emotion Recognizer Quick Start: To run the model on test sets of four datasets, Download th

39 Dec 24, 2022

A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

6 Oct 4, 2022

Speech Emotion Recognition with Fusion of Acoustic- and Linguistic-Feature-Based Decisions

APSIPA-SER-with-A-and-T This code is the implementation of Speech Emotion Recognition (SER) with acoustic and linguistic features. The network model i

3 Jan 4, 2023

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

65 Dec 20, 2022

Identify the emotion of multiple speakers in an Audio Segment

MevonAI - Speech Emotion Recognition Identify the emotion of multiple speakers in a Audio Segment Report Bug · Request Feature Try the Demo Here Table

110 Dec 3, 2022

RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

1 Oct 5, 2021

Releases(v0.1.1)

v0.1.1(Apr 19, 2022)

This is NaijaSenti dataset first release ! We would appreciate feedback. In the subsequent release, we will release the individual tweet annotation.
Source code(tar.gz)
Source code(zip)
data.zip(7.67 MB)

NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages

Related tags

Overview

Table of Contents

Paper

Abstract

Download NaijaSenti Datasets

1. Manually Annotated Twitter Sentiment Dataset

2. Manually Annotated Sentiment Lexicon

3. Semi-automatically Translated emotion lexicon

4. Semi-automatically Translated sentiment lexicon

5. Large Scale Unlabled Twitter Sentiment Corpus

5. Stop-words for Hausa, Igbo, Pidgin and Yoruba

Model

Citation

Papers from this project

Contact us

Changelog

License

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