Abstract
Due to a shortage of resources for studying opinions and feelings in Arabic dialects, the task to identify the polarity of sentiments in the Arabic web is a challenging task. In this paper we present MYC a Moroccan YouTube Corpus of manually annotated comments with the aim of facilitating the task of sentiments analysis of Moroccan dialect in the web. Comments are collected from the wildly used website YouTube and manually annotated by several annotators. Using the voting approach, we created the largest Moroccan dialect subjectivity corpus of 20 000 comments labeled into positive and negative comments and including some other information (topic, likes and dislikes). This dataset could be a useful tool for the creation of Moroccan dialect-specific NLP applications in the future. In the trials, Support Vector Machines (SVM) and Naive Bayes (NB), two well-known supervised learning classifiers, were used to categorize comments as positive or negative, using a distinct set of parameters for each. Each classifier’s recall, precision, and F-measure are computed. Both SVM and NB do well in terms of precision. The acquired results encourage us to move further with additional Moroccan comments from different videos in order to generalize our model.
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Jbel, M., Hafidi, I., Metrane, A. (2023). MYC: A Moroccan Corpus for Sentiment Analysis. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_6
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