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

TL;DR

This paper investigates sentiment analysis in Misurata Arabic poetry using traditional machine learning classifiers and deep learning methods, highlighting the comparative performance and suggesting future research directions.

Contribution

It introduces sentiment detection in Misurata Arabic poetry and compares traditional classifiers with deep learning tools, revealing higher accuracy for traditional methods.

Findings

Traditional classifiers outperform Mazajak deep learning tool in accuracy.

Sentiment analysis in Arabic poetry remains challenging due to figurative language.

Further research needed on linguistic features affecting sentiment detection.

Abstract

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.