Emotion detection is playing a very important role in our life. People express their emotions in different ways i.e face expression, gestures, speech, and text. This research focuses on detecting emotions from the Roman Urdu text. Previously, A lot of work has been done on different languages for emotion detection but there is limited work done in Roman Urdu. Therefore, there is a need to explore Roman Urdu as it is the most widely used language on social media platforms for communication. One major issue for the Roman Urdu is the absence of benchmark corpora for emotion detection from text because language assets are essential for different natural language processing (NLP) tasks. There are many useful applications of the emotional analysis of a text such as improving the quality of products, dialog systems, investment trends, mental health. In this research, to focus on the emotional polarity of the Roman Urdu sentence we develop a comprehensive corpus of 18k sentences that are gathered from different domains and annotate it with six different classes. We applied different baseline algorithms like KNN, Decision tree, SVM, and Random Forest on our corpus. After experimentation and evaluation, the results showed that the SVM model achieves a better F-measure score.