The MAC is a free and large Moroccan Arabic corpus consisting of 18000 manually labeled tweets resulting in a lexicon-dictionary of 30000 words labeled as positive, negative and neutral. To the best of our knowledge, MAC is the first open and largest Moroccan Arabic corpus for sentiment analysis. It is pioneer by its size, its quality given by the consistency of the native annotators (IAA=0.9), and its accessibility to the research community. The MAC is benchmarked for forthcoming works through an exploratory data analysis carried out using the two-sentiment analysis approaches for polarity classification as well as language identification.

If you use the MAC corpus in a scientific publication, please consider citing at least one of the following papers:

• Garouani, M., Kharroubi, J. (2022). MAC: An Open and Free Moroccan Arabic Corpus for Sentiment Analysis. In: Ben Ahmed, M., Boudhir, A.A., Karaș, İ.R., Jain, V., Mellouli, S. (eds) Innovations in Smart Cities Applications Volume 5. SCA 2021. Lecture Notes in Networks and Systems, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-94191-8_68

@InProceedings{Garouani_MAC,
author="Garouani, Moncef
and Kharroubi, Jamal",
title="MAC: An Open and Free Moroccan Arabic Corpus for Sentiment Analysis",
booktitle="Innovations in Smart Cities Applications Volume 5",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="849--858",
doi="10.1007/978-3-030-94191-8_68"
}

• Garouani, M., Kharroubi, J. (2022). Towards a New Lexicon-Based Features Vector for Sentiment Analysis: Application to Moroccan Arabic Tweets. In: Maleh, Y., Alazab, M., Gherabi, N., Tawalbeh, L., Abd El-Latif, A.A. (eds) Advances in Information, Communication and Cybersecurity. ICI2C 2021. Lecture Notes in Networks and Systems, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-91738-8_7

@InProceedings{Garouani2022,
author="Garouani, Moncef
and Kharroubi, Jamal",
editor="Maleh, Yassine
and Alazab, Mamoun
and Gherabi, Noreddine
and Tawalbeh, Lo'ai
and Abd El-Latif, Ahmed A.",
title="Towards a New Lexicon-Based Features Vector for Sentiment Analysis: Application to Moroccan Arabic Tweets",
booktitle="Advances in Information, Communication and Cybersecurity",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="67--76",
doi="10.1007/978-3-030-91738-8_7"
}

• Garouani, M., Chrita, H., Kharroubi, J. (2021). Sentiment Analysis of Moroccan Tweets Using Text Mining. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_54

@InProceedings{Garouani2021,
author="Garouani, Moncef
and Chrita, Hanae
and Kharroubi, Jamal",
editor="Motahhir, Saad
and Bossoufi, Badre",
title="Sentiment Analysis of Moroccan Tweets Using Text Mining",
booktitle="Digital Technologies and Applications",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="597--608",
doi="10.1007/978-3-030-73882-2_54"
}