Movement control orders are usually implemented during a pandemic that has spread in a particular country or region. A coronavirus-related pandemic occurred around the world in 2020. The declaration of the World Health Organization indicates that this virus affects millions of lives and has become a significant threat to human life globally. During the implementation of the movement control order, the use of Online Social Networks (OSNs) such as Twitter was excessive. Many OSN users use this platform to express their feelings and interact visually. This study aims to develop a sentiment analysis for emotion divergence using OSNs during movement control orders. The study considered data collection from Twitter for two movement control durations. The study will analyze and investigate people from eight emotional polarities: Anger, Expectation, Joy, Belief, Sadness, Fear, Disgust, and sentiment polarities, such as the Classification of positive and negative sentiments. Near 20,000 tweets in English were collected and pre-processed, and the Syuzhet package in the R language was used for sentiment analysis. The results showed that the emotions of fear and sadness had a sizeable emotional difference, and the decrease in data between the two movement controls supported by the difference in the emotion of joy was increasing.