[Paper Review] Sentiment Analysis for Arabic in Social Media Network: A Systematic Mapping Study

M. E. Abo, Ram Gopal Raj+2 more|arXiv (Cornell University)|Oct 26, 2019

Sentiment Analysis and Opinion Mining73 references12 citations

TL;DR

This systematic mapping study analyzes 51 primary studies on Arabic Sentiment Analysis (ASA) in social media from 2015 onward, using evidence-based methods to map research trends, methodologies, and contributions. It reveals a growing research field dominated by solution-focused studies, with key trends in evaluation, validation, and emerging techniques in deep learning and hybrid models.

ABSTRACT

With the expansion in tenders on the Internet and social media, Arabic Sentiment Analysis (ASA) has assumed a significant position in the field of text mining study and has since remained used to explore the sentiments of users about services, various products or topics conversed over the Internet. This mapping paper designs to comprehensively investigate the papers demographics, fertility, and directions of the ASA research domain. Furthermore, plans to analyze current ASA techniques and find movements in the research. This paper describes a systematic mapping study (SMS) of 51 primary selected studies (PSS) is handled with the approval of an evidence-based systematic method to ensure handling of all related papers. The analyzed results showed the increase of both the ASA research area and numbers of publications per year since 2015. Three main research facets were found, i.e. validation, solution, and evaluation research, with solution research becoming more treatment than another research type. Therefore numerous contribution facets were singled out. In totality, the general demographics of the ASA research field were highlighted and discussed

Motivation & Objective

• To comprehensively map the state of research in Arabic Sentiment Analysis (ASA) within social media contexts.
• To identify key research trends, methodologies, and thematic clusters in ASA literature.
• To analyze publication trends, research focus distribution, and methodological evolution in ASA research.
• To highlight dominant research types—particularly solution-focused studies—and their contributions.
• To provide a structured overview of the ASA research domain for future research guidance and benchmarking.

Proposed method

• Conducted a systematic mapping study (SMS) following evidence-based protocols to ensure comprehensive retrieval and analysis of relevant literature.
• Selected 51 primary studies (PSS) through predefined inclusion and exclusion criteria from a broader pool of publications.
• Classified studies based on research type: validation, solution, and evaluation, using thematic and methodological coding.
• Analyzed publication trends, research focus distribution, and methodological approaches across years (2015–2019).
• Used qualitative and quantitative synthesis to identify recurring techniques, tools, and frameworks in ASA research.
• Employed visualizations (figures and tables) to represent research demographics, publication trends, and methodological evolution.

Experimental results

Research questions

• RQ1 What are the dominant research types (validation, solution, evaluation) in Arabic Sentiment Analysis (ASA) research on social media?
• RQ2 How has the volume of ASA research publications evolved from 2015 to 2019?
• RQ3 What are the most frequently used methodologies and techniques in ASA studies?
• RQ4 Which research gaps and emerging trends are evident in the current ASA literature?
• RQ5 How do the methodological and thematic characteristics of ASA studies vary over time?

Key findings

• The ASA research domain has experienced a significant increase in publications since 2015, indicating growing academic and practical interest.
• Solution-focused research is the most prevalent research type, surpassing validation and evaluation studies in frequency.
• A growing number of studies employ deep learning and hybrid models, reflecting a shift toward advanced NLP techniques.
• Evaluation and validation remain critical but less dominant, suggesting a need for more standardized benchmarks and datasets.
• The majority of studies focus on modern dialects and informal Arabic, highlighting challenges in handling linguistic variation.
• There is a noticeable trend toward using multilingual and pre-trained models, though Arabic-specific models remain underdeveloped.