A Deep Learning Model for Identifying and Analyzing Sarcasm and Emotions in Lebanese Arabizi from Instagram and Twitter Data

Abstract

People use informal language on microblog platforms to share their opinions on products, events, sports, or politics. Moreover, microblog platforms often harbor instances of hate speech and cyberbullying, resulting in a massive amount of data available for natural language processing applications. Most studies have predominantly focused on common languages like English for tasks such as hate speech detection, sentiment analysis, and emotion analysis. Dialectal Arabic presents additional challenges due to its morphological richness and complexity, making NLP applications more intricate. While recent research has explored Arabic and Arabizi dialects, there has been limited attention given to Lebanese Arabizi. To address this gap, our objective was to construct a substantial Lebanese Arabizi dataset and make it accessible for NLP research. Additionally, we sought to develop a new approach to Arabizi detection and explored the identification of sarcasm and emotion recognition. The dataset comprised 11,000 rows, a combination of comments collected from Instagram and tweets. We utilized a pre-trained DziriBERT model for Arabizi identification and sarcasm detection, comparing the performances of contextual embedding and semantic embedding models. The word embeddings were then input into a Bidirectional Long Short-Term Memory (BiLSTM) model for emotion recognition. The Arabizi identification model achieved an impressive macro F1 score of 98%, while the sarcasm detection model achieved an average macro F1 score of 63%. This Arabizi detection model not only contributes to expanding the Arabizi dataset but also holds potential for broader applications. Sarcasm detection is crucial for microblog platforms to filter content, particularly since it heavily relies on the manual reporting of offensive material. Additionally, emotion recognition assists companies in understanding customers’ opinions about their products and services.