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Over the recent decades, there has been a significant increase anddevelopment of resources for Arabic natural language processing. This includesthe task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabicutterances in both Modern Standard Arabic (MSA) and different Arabic dialects.This study focuses on detecting sentiment in poems written in Misurata Arabicsub-dialect spoken in Misurata, Libya. The tools used to detect sentiment fromthe dataset are Sklearn as well as Mazajak sentiment tool 1. LogisticRegression, 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 classifiersscore a higher level of accuracy as compared to Mazajak which is built on analgorithm that includes deep learning techniques. More research is suggested toanalyze Arabic sub-dialect poetry in order to investigate the aspects thatcontribute to sentiments in these multi-line texts; for example, the use offigurative language such as metaphors.

Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect