Sentiment Analysis in Arabic Text and Emoji Using Deep Learning Methods

The advent of social media has simplified the rapid publishing of explanations on inclusive announcements, movies, politics, and the economy. This growth has led to an increase in the breadth of topics covered. This emotion analysis includes many aspects. Arabic and OMCD survey big data were integrated with data from Twitter to inform this study. Different word embedding methods were implemented, such as Spacy (W2V), FastText, and Arabic Bidirectional Encoder Representation (AraBERT). In the context of sentiment analysis models, convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were employed. The evaluation of model performance was based on accuracy. Deep learning (DL) methods using the AST (Arabic sentiment Twitter) dataset yielded 72% and 95% model accuracy rates. The accuracy rates for the OMCD (Offensive Moroccan Comments Dataset) is a dataset containing offensive comments in the Moroccan dialect. dataset fell within the range of 54% to 84%.