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

• URL: http://arxiv.org/abs/2502.09128v1
• Date: Thu, 13 Feb 2025 10:05:44 GMT
• Title: A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions
• Authors: Nasser A Alsadhan,
• Abstract summary: Current research in emotion detection in the Arabic language lacks awareness of how emotions are exhibited in different dialects. This research builds a novel framework that can identify and predict Arabic dialects and emotions from a given text. It achieved an accuracy of 88.9% in classifying Arabic dialects, which outperforms the state-of-the-art results by 6.45 percentage points.
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• 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.

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