Abstract
in the last few years, Twitter becomes the most popular platform for individuals to share their experiences and viewpoints towards different products and services. Therefore, it attracts a lot of researchers to use it as a body for sentiment analysis and opinion mining research studies. Most of the previous research studies in this area have been using the traditional machine learning-based and lexicon-based approaches more compared to the deep learning approach to classify the emotional states of English tweets. Also, there is a shortage of research studies that categorize the opinion orientations of tweets in other languages such as Arabic. Recently, deep learning approach has achieved remarkable results over the traditional machine learning algorithms in analyzing a massive amount of data as the case with social networks data. In this research study, we seek to discuss the state-of-the-art of sentiment analysis methodologies used to classify tweets' sentiment orientation and challenges that need to be addressed. Also, this paper provides an overview of deep learning approach and question if this approach can be adopted to improve the classification accuracy of sentiment analysis for both English and Arabic tweets.