# SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

arXiv: https://arxiv.org/abs/1808.05079 · HTML: https://mlsanity.com/paper/1808.05079/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://mlsanity.com/paper/2104.01443/) (2021)
- [BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews](https://mlsanity.com/paper/2107.06802/) (2021)
- [TArC: Tunisian Arabish Corpus First complete release](https://mlsanity.com/paper/2207.04796/) (2022)
- [FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis](https://mlsanity.com/paper/2411.04604/) (2024)
- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)
- [An Enhanced Corpus for Arabic Newspapers Comments](https://mlsanity.com/paper/2102.09965/) (2021)

## Abstract (arXiv)

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the difficulties associated with an appropriate data annotation has been underestimated. In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (a Maghrebi Arabic dialect). The construction of this corpus is based on an Algerian sentiment lexicon that is also constructed automatically. The presented work deals with the two widely used scripts on Arabic social media: Arabic and Arabizi. The proposed approach automatically constructs a sentiment corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi test sets, respectively. Ongoing work is aimed at integrating transliteration process for Arabizi messages to further improve the obtained results.