On social media, Arabic speakers tend to express themselves in their own local dialect. To do so, Tunisians use ‘Tunisian Arabizi’, where the Latin alphabet is supplemented with numbers. However, annotated datasets for Arabizi are limited; in fact, this challenge uses the only known Tunisian Arabizi dataset in existence.
Sentiment analysis relies on multiple word senses and cultural knowledge, and can be influenced by age, gender and socio-economic status.
For this task, we have collected and annotated sentences from different social media platforms. The objective of this challenge is to, given a sentence, classify whether the sentence is of positive, negative, or neutral sentiment. For messages conveying both a positive and negative sentiment, whichever is the stronger sentiment should be chosen. Predict if the text would be considered positive, negative, or neutral (for an average user). This is a binary task.
Such solutions could be used by banking, insurance companies, or social media influencers to better understand and interpret a product’s audience and their reactions.
This competition is one of five NLP challenges we will be hosting on Zindi as part of AI4D’s ongoing African language NLP project, and is a continuation of the African language dataset challenges we hosted earlier this year. You can read more about the work here.
iCompass is a Tunisian startup, created in July, 2019 and labelled startup act in August 2019. iCompass is specialized in the Artificial Intelligence field, and more particularly in the Natural Language Processing field. The particularity of iCompass is breaking the language barrier by developing systems that understand and interpret local dialects, especially African and Arab ones.