  Over the recent decades, there has been a significant increase and
development of resources for Arabic natural language processing. This includes
the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic
utterances in both Modern Standard Arabic (MSA) and different Arabic dialects.
This study focuses on detecting sentiment in poems written in Misurata Arabic
sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from
the dataset are Sklearn as well as Mazajak sentiment tool 1. Logistic
Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM)
classifiers are used with Sklearn, while the Convolutional Neural Network (CNN)
is implemented with Mazajak. The results show that the traditional classifiers
score a higher level of accuracy as compared to Mazajak which is built on an
algorithm that includes deep learning techniques. More research is suggested to
analyze Arabic sub-dialect poetry in order to investigate the aspects that
contribute to sentiments in these multi-line texts; for example, the use of
figurative language such as metaphors.
