Emotions are a part of everyday communications of people and one of the important elements of human nature. We can distinguish a person’s emotions from some outcome behaviors such as speech, facial expression, body movements, and gestures. Another outcome behavior is his/her grammar and written method that reflects the inner states of the person. Since people are nowadays more likely to use textual tools to make the connection, emotion extraction from the text has attracted much attention. The majority of methods in this regard consider emotion extraction from the text as a classification problem. Therefore, most studies depend on a huge number of handcrafted features and are done on feature engineering to enhance the classification performance. Considering that a text may include more than one emotion that only one of them is text dominant emotion, we model the emotion extraction problem as a multi-label classification problem by removing the fixed boundaries of emotions. Next, we recognize all the existing emotions in the sentence and in dominant emotion. Our goal is to achieve a better performance only with minimal feature engineering. To this end, we propose a hybrid deep learning model that benefits both CNN and RNN deep models. The experiments are done on a multi-label dataset including 629 sentences with eight emotional categories. Based on the results, our proposed method shows a better performance (about 0.12%) compared with available multi-label learning methods (e.g., BR, RAKEL, and MLkNN).