A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions

TL;DR

This paper introduces a dialect-aware framework for classifying Arabic dialects and emotions, utilizing a novel lexicon-building approach that improves accuracy over existing methods.

Contribution

It presents a new framework with modules for text preprocessing, classification, and clustering, including a novel dialect-aware emotion lexicon for Arabic text analysis.

Findings

Achieved 88.9% dialect classification accuracy, surpassing previous state-of-the-art by 6.45 percentage points.

Attained 89.1% and 79% accuracy in emotion detection for Egyptian and Gulf dialects.

Developed a new dialect-specific emotional lexicon for Arabic language processing.

Abstract

Arabic is one of the oldest languages still in use today. As a result, several Arabic-speaking regions have developed dialects that are unique to them. Dialect and emotion recognition have various uses in Arabic text analysis, such as determining an online customer's origin based on their comments. Furthermore, intelligent chatbots that are aware of a user's emotions can respond appropriately to the user. Current research in emotion detection in the Arabic language lacks awareness of how emotions are exhibited in different dialects, which motivates the work found in this study. This research addresses the problems of dialect and emotion classification in Arabic. Specifically, this is achieved by building a novel framework that can identify and predict Arabic dialects and emotions from a given text. The framework consists of three modules: A text-preprocessing module, a classification module, and a clustering module with the novel capability of building new dialect-aware emotion lexicons. The proposed framework generated a new emotional lexicon for different dialects. It achieved an accuracy of 88.9% in classifying Arabic dialects, which outperforms the state-of-the-art results by 6.45 percentage points. Furthermore, the framework achieved 89.1-79% accuracy in detecting emotions in the Egyptian and Gulf dialects, respectively.