7. A non-transitory computer-readable storage medium including instructions stored thereon that when processed by at least one processor cause an integrated system to perform operations comprising: receiving textual utterance data, audio utterance data and visual utterance data; fetching a set of facial expressions from the visual utterance data; annotating the set of facial expressions with corresponding set of emotions using predictive modeling; generating labelled data by tagging at least one of the textual utterance data and the audio utterance data with the set of emotions based on the set of facial expressions; providing the labelled data and non-labelled data to a self-learning model of the integrated system, wherein the non-labelled data comprises new textual utterance data received from a user, and wherein the self-learning model, learns, from the labelled data, about the set of emotions tagged with the textual utterance data, determines a new set of emotions corresponding to the new textual utterance data by using at least one of type of a recurrent neural network (RNN), generates new labelled data by tagging the new textual utterance data with the new set of emotions, and integrates the new labelled data into the self-learning model, thereby updating the self-learning model; and converting the audio utterance data into corresponding textual utterance data by using speech-to-text translation.
7. A non-transitory computer-readable storage medium including instructions stored thereon that when processed by at least one processor cause an integrated system to perform operations comprising: receiving textual utterance data, audio utterance data and visual utterance data; fetching a set of facial expressions from the visual utterance data; annotating the set of facial expressions with corresponding set of emotions using predictive modeling; generating labelled data by tagging at least one of the textual utterance data and the audio utterance data with the set of emotions based on the set of facial expressions; providing the labelled data and non-labelled data to a self-learning model of the integrated system, wherein the non-labelled data comprises new textual utterance data received from a user, and wherein the self-learning model, learns, from the labelled data, about the set of emotions tagged with the textual utterance data, determines a new set of emotions corresponding to the new textual utterance data by using at least one of type of a recurrent neural network (RNN), generates new labelled data by tagging the new textual utterance data with the new set of emotions, and integrates the new labelled data into the self-learning model, thereby updating the self-learning model; and converting the audio utterance data into corresponding textual utterance data by using speech-to-text translation. 9. The integrated system as claimed in claim 7 , wherein the at least one of type of the RNN comprises at least one of a gated recurrent unit (GRU) and a long short term memory (LSTM) network.