While having a conversation, it becomes important to understand the emotion of the opposite party in order to converse more effectively. However, in order to avoid unwanted situations it is also important to identify the cause of those emotions as well. Detecting the reason behind emotions and their shift, sometimes, comes as a challenge even for human beings let alone for machines. In this work, we try to train a model which can not only identify the current emotion of a speaker but can also reason out the shift in the emotional state of a speaker.