Abstract
Text-based communication systems often lead to misinterpretation of a message because the emotional context is lost. A recipient may infer a sender’s sentiment based solely on the text, leading to misunderstandings. This disclosure describes a method to supplement text messages (or other communications) with representations of the sender’s inferred sentiment, captured during composition. The technique involves collecting multi-modal biological information from the sender, such as facial expressions, gaze, and gestures, using sensors on the composing device. A machine learning model can analyze this data to infer the sender’s sentiment. A graphical representation, like an emoji, corresponding to the inferred sentiment can then associated with the message. The primary purpose is to provide crucial emotional context to the recipient, thereby reducing misinterpretation and fostering clearer communication.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Najaran, Mahdi Tayarani and Du, Ruofei, "Communication Supplementation Based On Sentiment Analysis", Technical Disclosure Commons, (November 19, 2025)
https://www.tdcommons.org/dpubs_series/8905