#

## Paper
Title: `AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages`

Paper Link: https://aclanthology.org/2023.emnlp-main.862/

## Abstract
>Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the AfriSenti datasets and discuss their usefulness.

HomePage: https://github.com/afrisenti-semeval/afrisent-semeval-2023

### Citation

```
@inproceedings{muhammad-etal-2023-afrisenti,
    title = "{A}fri{S}enti: A {T}witter Sentiment Analysis Benchmark for {A}frican Languages",
    author = "Muhammad, Shamsuddeen Hassan  and
      Abdulmumin, Idris  and
      Ayele, Abinew Ali  and
      Ousidhoum, Nedjma  and
      Adelani, David Ifeoluwa  and
      Yimam, Seid Muhie  and
      Ahmad, Ibrahim Sa'id  and
      Beloucif, Meriem  and
      Mohammad, Saif M.  and
      Ruder, Sebastian  and
      Hourrane, Oumaima  and
      Brazdil, Pavel  and
      Jorge, Alipio  and
      Ali, Felermino D{\'a}rio M{\'a}rio Ant{\'o}nio  and
      David, Davis  and
      Osei, Salomey  and
      Shehu Bello, Bello  and
      Ibrahim, Falalu  and
      Gwadabe, Tajuddeen  and
      Rutunda, Samuel  and
      Belay, Tadesse  and
      Messelle, Wendimu Baye  and
      Balcha, Hailu Beshada  and
      Chala, Sisay Adugna  and
      Gebremichael, Hagos Tesfahun  and
      Opoku, Bernard  and
      Arthur, Stephen",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.862/",
    doi = "10.18653/v1/2023.emnlp-main.862",
    pages = "13968--13981",
    abstract = "Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of {\ensuremath{>}}110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the AfriSenti datasets and discuss their usefulness."
}
```