Darija Open Dataset (DODa) represents an open-source project aimed at
enhancing Natural Language Processing capabilities for the Moroccan dialect,
Darija. With approximately 100,000 entries, DODa stands as the largest
collaborative project of its kind for Darija-English translation. The dataset
features semantic and syntactic categorizations, variations in spelling, verb
conjugations across multiple tenses, as well as tens of thousands of translated
sentences. The dataset includes entries written in both Latin and Arabic
alphabets, reflecting the linguistic variations and preferences found in
different sources and applications. The availability of such dataset is
critical for developing applications that can accurately understand and
generate Darija, thus supporting the linguistic needs of the Moroccan community
and potentially extending to similar dialects in neighboring regions. This
paper explores the strategic importance of DODa, its current achievements, and
the envisioned future enhancements that will continue to promote its use and
expansion in the global NLP landscape.

DODa 是一个旨在增强对摩洛哥方言 Darija 的自然语言处理能力的开源项目，拥有大约 100,000 个条目，是 Darija-English 翻译领域最大的协作项目，包含语义和语法分类、拼写变化、多种时态的动词变位以及成千上万个翻译的句子。该数据集反映了语言上的变体和偏好，包括使用拉丁字母和阿拉伯字母书写的条目，对于开发准确理解和生成 Darija 的应用程序至关重要，支持摩洛哥社区的语言需求，并可能扩展到邻近地区的类似方言。本文探讨了 DODa 的战略重要性、目前的成果以及未来的增强计划，旨在促进其在全球自然语言处理领域的使用和发展。

Darija 开放数据集的演变：介绍第 2 版

The Evolution of Darija Open Dataset: Introducing Version 2

With the rapid growth of the use of social media websites, obtaining the
users' feedback automatically became a crucial task to evaluate their
tendencies and behaviors online. Despite this great availability of
information, and the increasing number of Arabic users only few research has
managed to treat Arabic dialects. The purpose of this paper is to study the
opinion and emotion expressed in real Moroccan texts precisely in the YouTube
comments using some well-known and commonly used methods for sentiment
analysis. In this paper, we present our work of Moroccan dialect comments
classification using Machine Learning (ML) models and based on our collected
and manually annotated YouTube Moroccan dialect dataset. By employing many text
preprocessing and data representation techniques we aim to compare our
classification results utilizing the most commonly used supervised classifiers:
k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and
deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and
Long Short-Term Memory (LTSM). Experiments were performed using both raw and
preprocessed data to show the importance of the preprocessing. In fact, the
experimental results prove that DL models have a better performance for
Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.

本文目的在于通过情感分析依据机器学习模型对 YouTube 摩洛哥方言评论进行分类研究，在采用多种文本预处理和数据表现技术的前提下，证明深度学习模型胜于传统模型，我们达到了 90% 的准确性。