A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Abstract

Due to the significant use of Arabic language in social media networks, the demand for Arabic sentiment analysis has increased rapidly. Although, numerous sentiment analysis techniques enable people to obtain valuable insights from the opinions shared on social media. However, these techniques are still in their infancy, and the Arabic sentiment analysis domain lacks a compressive survey. Therefore, this study focused on the various characteristics, State-of-the-Art, and the level of sentiment analysis along with the natural language processing applied in the Arabic sentiment analysis. Furthermore, this study also discussed the sentiment analysis of the modern standards and the dialects of Arabic languages along with various machine learning processes and a few popular algorithms. Moreover, this study adds values by critical analysis of two case studies, which displayed an extensive set of the various research communities in this field of sentiment analysis. Finally, open research challenges are investigated, with a focus on the shortage of lexicons; availability; use of Dialect Arabic (DA); lack of corpora and datasets; right to left reading and compound phrases and idioms.

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

“…The survey starts with extracting relevant papers and information from recent comprehensive ASA review papers [1], [2], [10], followed by a focused review of the collected papers to extract models, features and datasets.…”

“…According to [1], [2], [10], most of the surveyed ASA publications have adopted shallow machine learning models that have existed for many years. Support vector machines (SVMs) and naïve Bayes classifiers are the most dominant approaches, accounting for more than 70% of the reported models.…”

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

“…Due to the increased amount of data from user-generated content on social media, text classification has become an important area of research in the last 10 years. This has led researchers to apply text classification methods for analyzing sentiments and topics [ 1 – 3 ], predicting gender [ 4 – 6 ], and detecting false news [ 7 , 8 ]. Studies on social media have indicated that, as a wide variety of people use this medium to share health information [ 9 , 10 ], the information provided is not always accurate [ 11 , 12 ] and this is a huge issue of concern.…”

“…There have been many studies of text classification regarding Arabic natural language processing on social media. Most of them are focused on sentiment analysis, and a number of literature surveys and systematic literature reviews have been conducted on this Arabic-language-classification-specific task [ 1 – 3 ]. More specifically, Al-Rubaiee et al [ 19 ], Alayba et al [ 20 ], and Alabbas et al [ 21 ] conducted targeted sentiment-analysis studies.…”

Annotated Corpus of Mesopotamian-Iraqi Dialect for Sentiment Analysis in Social Media

“…Regardless of the Important of Mesopotamian-Iraqi Dialect (MID) in the world (and Arabic Language in general), studies on Sentiment Analysis in social media using this dialect is so rare and there is no real dataset developed in MID neither an annotated corpus that can be relay on for the sentiment analysis of social media in this dialect [2]. Some Researchers preferred to do their researches on the English version on the original Arabic text instead, because of the complexity of Arabic language in general and the features that facilitates the extracting of the result in the English language to get a more accurate result [3].…”