Attention Bidirectional Recurrent Neural Zero-Shot Semantic Classifier for Emotional Footprint Identification

Exploring emotions in organization settings, particularly in feedback on organizational welfare programs, is critical for understanding employee experiences and enhancing organizational policies. Recognizing emotions from a conversation (i.e., leaving an emotional footprint) is a predominant task for a machine to comprehend the full context of the conversation. While fine-tuning of pre-trained models has invariably provided state-of-the-art results in emotion footprint recognition tasks, the prospect of a zero-shot learned model in this sphere is, on the whole, unexplored. The objective here remains to identify the emotional footprint of the members participating in the conversation after the conversation is over with improved accuracy, time and minimal error rate. To address these gaps, in this work, a method called Attention Bidirectional Recurrent Neural Zero-Shot Semantic Classifier (ABRN-ZSSC) for emotional footprint identification is proposed. The ABRN-ZSSC for emotional footprint identification is split into two sections. First, the raw data from a Two-Party Conversation with Emotional Footprint and Emotional Intensity are subjected to the Attention Bidirectional Recurrent Neural Network model with the intent of identifying the emotional footprint for each party near the conclusion of the conversation and, second, with the identified emotional footprint in a conversation. The Zero-Shot Learning-based classifier is applied to train and classify emotions both accurately and precisely. We verify the utility of these approaches (i.e., emotional footprint identification and classification) by performing an extensive experimental evaluation on two corpora on four aspects, training time, accuracy, precision, and error rate for varying samples. Experimental results demonstrate that the ABRN-ZSSC method outperforms two existing baseline models in emotion inference tasks across the dataset. An outcome of the proposed ABRN-ZSSC method is that it obtains superior performance in terms of 10% precision, 17% accuracy and 8% recall as well as 19% training time and 18% error rate compared to the conventional methods.