Human emotions can be expressed in many ways through facial expressions, speech, actions, and in textual form. Emotions play an important role in determining the human's state and influence their lives in decision-making, behaviour and interaction. With the evolution of many advances in technology, researchers are able to detect the emotion of humans through facial expressions, speech, and text. Much advanced and efficient research work has been done in the field of emotion recognition through facial expression and audio, but still, there are not great works on emotion detection from text. Detecting emotions from text has wide applications in stock markets, business, decision-making, analyzing the view of people on any topic through social media conversations, tweets, blogs, and articles. Our work is focused on the detection of emotions from the text. We propose different approaches for detecting emotions through text. We implemented different approaches like the lexicon-based approach, the supervised machine learning approach with the Naïve-bayes algorithm, and the unsupervised machine learning approach with semantic similarities. After the consideration of the cons of all the approaches and the accuracies derived from our work, it is evident that the unsupervised machine learning approach with semantic similarities is the best approach with an accuracy of 78.5%.