Sentiment analysis, also called opinion mining, is an extensive research field due to the rapid growth in social media. It has widespread applications in almost all areas of today’s life, from business services to political field. Sentiment analysis refers to the opinion or feelings of a person toward a particular topic. It gives results or a subjective impression, not facts, and can be expressed as a polarity to negative, positive, or neutral. Sentiments can be analyzed by utilizing statistics, natural language programming, or machine learning techniques. These techniques are implemented on data previously collected from social networking sites or blogs, the most famous of which is Twitter. Twitter is one of the essential sources of people’s opinions on various topics. This source permits individuals to state their views and offer different perspectives on any field. Many organizations have become interested in analyzing people’s feelings through social networks, especially in political and economic domains. The main task is to classify the level of messages or tweets to their polarity. In this research, we will look at the most important approaches used in sentiment analysis and how they process the collected data. An explanation of the feature extraction methods will be presented. We will also clarify the levels of sentiment analysis and the challenges facing sentiment analysis.