A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

Abstract

People in the Arab world prefer to use their dialects in writing while using social media sites which results in generating a significant amount of texts. Sentiment analysis in texts is one of the most important applications of the Natural Language Processing field of science. Sentiment Analysis involves classifying texts based upon emotions or polarity. Different approaches have been utilized so far for Sentiment Analysis of Arabic Texts. In this systematic review, the author aims to explore the researches and studies that were in the field of Sentiment Analysis of Arabic Dialects to report the utilized approaches, constructed datasets, most common type of dialects explored by researchers, and reported results of Sentiment Analysis in term of accuracy, recall, precision, and F-Measure. Author reported the following findings for this systematic review: (1) in most of the included research papers the dialect type is not specified and mostly it was mentioned (Arabic Dialects) in general, however, some other dialects were explored specifically by authors such as Saudi Dialect (2) 50% of the utilized datasets in the included research papers are constructed by the authors themselves, moreover, the size of most of the utilized datasets are between 10,000 and 50,000, and very limit number of datasets had size above 100,000, (3) machine learning algorithms (classifiers) are the most common approach that were used for Sentiment Analysis of Arabic Dialects (4) the best result for Sentiment Analysis of Arabic Dialects achieved while different machine learning algorithms (classifiers) were used, and (5) among social media platforms, Twitter is the most common utilized online platform for constructing datasets for Arabic Dialects texts.

Sentiment Analysis of Emirati Dialect

“…The researchers also found that, when compared to other Arab world nations, the Gulf countries have the lowest Facebook usage [10]. It was also found that most researchers used the Twitter and Facebook platforms to create Arabic text datasets [6].…”

“…(b) According to [40], Instagram is one of the top three social media platforms in the United Arab Emirates; Instagram is popular social media site in UAE and its popularity is increasing day after day. (c) Finally, while reviewing research papers, most of the constructed datasets used Twitter and Facebook platforms to construct datasets of Arabic texts; however, a limited number of studies targeted the Instagram platform for collecting comments and constructing datasets from the collected comments [6].…”

“…In our most recent work, in [6,8], we reported that the majority of the constructed corpus in previous works that focused on the Arabic language either focused on the MSA form or Arabic dialects but did not specify a specific dialect [6], or these studies focused on Twitter and Facebook [6]. Our study addresses this gap in the research by focusing on studying a specific Arabic dialect (of the United Arab Emirates), using text extracted from Instagram (which was shown to be the dominant social media website in the UAE [9]).…”

“…Although there are a lot of studies on the analysis and classification of texts from social media sites, such as Twitter and Facebook, there are relatively few studies that use the Instagram platform to construct datasets and build resources for Arabic dialects [6].…”

“…Most previous research that used a sentiment analysis for the Arabic language focused on Modern Standard Arabic [7]. The most common dialects explored in sentiment analyses are Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine dialects [6]. However, to our knowledge, no researchers have used a sentiment categorization for the Emirati dialect.…”