Algerian Dialect

TL;DR

The paper addresses the resource gap for sentiment analysis in Algerian Arabic by presenting a large-scale, manually annotated YouTube comment dataset (45,000 entries) with a five-class sentiment scheme and rich metadata. It applies careful annotation protocols and analyzes linguistic features like emojis and code-switching, validating the dataset with transformer-based baselines. The work delivers a valuable resource for dialectal NLP, enabling robust sentiment modeling, benchmarking, and cross-dialect research, with practical implications for social media analytics and public opinion studies. Overall, it advances Algerian dialect sentiment analysis by combining realistic data, rigorous labeling, and practical preprocessing guidance.

Abstract

We present Algerian Dialect, a large-scale sentiment-annotated dataset consisting of 45,000 YouTube comments written in Algerian Arabic dialect. The comments were collected from more than 30 Algerian press and media channels using the YouTube Data API. Each comment is manually annotated into one of five sentiment categories: very negative, negative, neutral, positive, and very positive. In addition to sentiment labels, the dataset includes rich metadata such as collection timestamps, like counts, video URLs, and annotation dates. This dataset addresses the scarcity of publicly available resources for Algerian dialect and aims to support research in sentiment analysis, dialectal Arabic NLP, and social media analytics. The dataset is publicly available on Mendeley Data under a CC BY 4.0 license at https://doi.org/10.17632/zzwg3nnhsz.2.

Figures (1)

• Figure 1: Preview of selected entries from the Algerian Dialect dataset showing comment text and associated sentiment labels.