Abstract: Described, herein, relates to a system and method of accurately and efficiently detecting a tone of a batch of text that achieves both high performance and interpretability by leveraging a multi-head self-attention architecture. The presence of a tone in a batch of text provides a challenge to the performance of a sentiment analysis, since there is an inherent ambiguity in sarcastic expressions that makes sarcasm detection difficult through purely text-based interactions. As such, the multi-head self-attention architecture includes distinct components of routing data through modules comprising data pre-processing, multi-head self-attention, gated recurrent units, and classification, resulting in a modeling system with improved interpretability and accuracy.

Abstract: Described, herein, relates to a system and method of accurately and efficiently detecting a tone of a batch of text that achieves both high performance and interpretability by leveraging a multi-head self-attention architecture. The presence of a tone in a batch of text provides a challenge to the performance of a sentiment analysis, since there is an inherent ambiguity in sarcastic expressions that makes sarcasm detection difficult through purely text-based interactions. As such, the multi-head self-attention architecture includes distinct components of routing data through modules comprising data pre-processing, multi-head self-attention, gated recurrent units, and classification, resulting in a modeling system with improved interpretability and accuracy.