# Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach

arXiv: https://arxiv.org/abs/1805.08533 · HTML: https://mlsanity.com/paper/1805.08533/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 3 of 3)

- [Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis](https://mlsanity.com/paper/2403.01921/) (2024)
- [From Human Annotation to Automation: LLM-in-the-Loop Active Learning for Arabic Sentiment Analysis](https://mlsanity.com/paper/2509.23515/) (2025)
- [Enhanced Arabic-language cyberbullying detection: deep embedding and transformer (BERT) approaches](https://mlsanity.com/paper/2510.02232/) (2025)

## Abstract (arXiv)

Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). The best F1-score for each of these models was (69.9,61.63,55.07) respectively.