We aim to extract emotional components within statements to identify the emotional state of the writer and assigning emoji related to the emotion. Emojis have become a staple part of everyday text-based communication. It is normal and common to construct an entire response with the sole use of emoji. It comes as no surprise, therefore, that effort is being put into the automatic prediction and selection of emoji appropriate for a text message. Major companies like Apple and Google have made immense strides in this, and have already deployed such systems into production (for example, the Google Gboard). The proposed work is focused on the problem of automatic emoji selection for a given text message using machine learning classification algorithms to categorize the tone of a message which is further segregated through n-gram into one of seven distinct categories. Based on the output of the classifier, select one of the more appropriate emoji from a predefined list using natural language processing (NLP) and sentimental analysis techniques. The corpus is extracted from Twitter. The result is a boring text message made lively after being annotated with appropriate text messages