# Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis

arXiv: https://arxiv.org/abs/2403.01921 · HTML: https://mlsanity.com/paper/2403.01921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 191 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Arabic Offensive Language on Twitter: Analysis and Experiments](https://mlsanity.com/paper/2004.02192/) (2020)
- [LABR: A Large Scale Arabic Sentiment Analysis Benchmark](https://mlsanity.com/paper/1411.6718/) (2014)
- [A Large Scale Corpus of Gulf Arabic](https://mlsanity.com/paper/1609.02960/) (2016)
- [Using Word Embeddings in Twitter Election Classification](https://mlsanity.com/paper/1606.07006/) (2016)
- [Lexicon Integrated CNN Models with Attention for Sentiment Analysis](https://mlsanity.com/paper/1610.06272/) (2016)
- [INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02748/) (2016)
- [Sentiment Analysis on YouTube: A Brief Survey](https://mlsanity.com/paper/1511.09142/) (2015)
- [Multichannel Variable-Size Convolution for Sentence Classification](https://mlsanity.com/paper/1603.04513/) (2016)
- [Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis](https://mlsanity.com/paper/1506.01906/) (2015)
- [Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach](https://mlsanity.com/paper/1805.08533/) (2018)
- [Using objective words in the reviews to improve the colloquial arabic sentiment analysis](https://mlsanity.com/paper/1709.08521/) (2017)
- [Arabic Language Sentiment Analysis on Health Services](https://mlsanity.com/paper/1702.03197/) (2017)
- [Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis](https://mlsanity.com/paper/1710.08451/) (2017)

## Built on by (most cited first, 1 of 1)

- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)

## Abstract (arXiv)

Sentiment analysis (SA) has been, and is still, a thriving research area. However, the task of Arabic sentiment analysis (ASA) is still underrepresented in the body of research. This study offers the first in-depth and in-breadth analysis of existing ASA studies of textual content and identifies their common themes, domains of application, methods, approaches, technologies and algorithms used. The in-depth study manually analyses 133 ASA papers published in the English language between 2002 and 2020 from four academic databases (SAGE, IEEE, Springer, WILEY) and from Google Scholar. The in-breadth study uses modern, automatic machine learning techniques, such as topic modelling and temporal analysis, on Open Access resources, to reinforce themes and trends identified by the prior study, on 2297 ASA publications between 2010-2020. The main findings show the different approaches used for ASA: machine learning, lexicon-based and hybrid approaches. Other findings include ASA 'winning' algorithms (SVM, NB, hybrid methods). Deep learning methods, such as LSTM can provide higher accuracy, but for ASA sometimes the corpora are not large enough to support them. Additionally, whilst there are some ASA corpora and lexicons, more are required. Specifically, Arabic tweets corpora and datasets are currently only moderately sized. Moreover, Arabic lexicons that have high coverage contain only Modern Standard Arabic (MSA) words, and those with Arabic dialects are quite small. Thus, new corpora need to be created. On the other hand, ASA tools are stringently lacking. There is a need to develop ASA tools that can be used in industry, as well as in academia, for Arabic text SA. Hence, our study offers insights into the challenges associated with ASA research and provides suggestions for ways to move the field forward such as lack of Dialectical Arabic resource, Arabic tweets, corpora and data sets for SA.