With the continued growth of curiosity in investigating public opinions on news, products, events, etc., many issues appear when executing the automatic procedure. Moreover, every language can have special characteristics that make executing techniques in other languages inaccurate, where the accuracy of final results is uncertain. Sentiment Analysis in Natural Language Processing (NLP) means utilizing computational linguistics methods to automatically extract, predict, and label the content polarity. It analyzes the reviews on social networks, interviews, phone calls, and blogs for products or services. Sentiment Analysis can be employed in several fields, such as marketing, decision-making, training, and medical systems. However, there needs to be more research recognizing these fields for the Arabic language. This study aims to develop a medical context sentiment extraction Artificial Intelligence model, whether they are written in Modern Standard Arabic (MSA), one of the Arabic Dialects, and/or Emoticons. A sizeable data collection of Arabic content was gathered from several social network websites (i.e., Facebook and Twitter), where the features were extracted and weighted.

Big data, Social network, Sentiment Analysis, computational linguistics, Artificial Intelligence, opinion mining.