A Deep Learning Approach for Sentiment and Emotional Analysis of Lebanese Arabizi Twitter Data

Arabizi is an Arabic dialect that is represented in Latin transliteration and is commonly used in social media and other informal settings. This work addresses the problem of Arabizi text identification and emotional analysis based on Lebanese dialect. The work starts with the extraction and construction of a dataset and uses two machine learning models. The first is based on fastText for learning the embeddings while the second uses a combination of recurrent and dense deep learning models. The proposed approaches were attempted on the Arabizi dataset that we extracted and curated from Twitter. We attempted our results with six classical machine learning approaches using separate sentiment and emotion analysis. We achieved the highest result in literature for the binary sentiment analysis with an F1 score of 81%. We also present baseline results for the 3-class sentiment classification of Arabizi tweets with an F1 score of 64%, and for emotion classification of Arabizi tweets with an f1 score of 61%.