Sentiment analysis involves using computational methods to identify and classify opinions expressed in text, with the goal of determining whether the writer's stance towards a particular topic, product, or idea is positive, negative, or neutral. However, sentiment analysis in Arabic presents unique challenges due to the complexity of Arabic morphology and the variety of dialects, which make language classification even more difficult. To address these challenges, we conducted to investigation and overview the techniques used in the last five years for embedding and classification of Arabic sentiment analysis (ASA). We collected data from 100 publications, resulting in a representative dataset of 2,300 detailed records that included attributes related to the dataset, feature extraction, approach, parameters, and performance measures. Our study aimed to identify the most powerful approaches and best model settings by analyzing the collected data to identify the significant parameters influencing performance. The results showed that Deep Learning and Machine Learning were the most commonly used techniques, followed by lexicon and transformer-based techniques. However, Deep Learning models were found to be more accurate for sentiment classification than other Machine Learning models. Furthermore, multi-level embedding was found to be a significant step in improving model accuracy.