LSTM Emotional Classification Method in Dialogue using Word-level Emotion Embedding based on Semi-Supervised Learning and LSTM model

摘要

For the method of classifying the emotions of utterances in a conversation using semi-supervised learning-based word unit emotion embedding and LSTM model according to an embodiment of the present invention, refer to a word emotion dictionary in which basic emotions are tagged for each word for learning. Thus, a word-unit emotion embedding step of tagging an emotion for each word in the speech of the input dialogue data; extracting an emotion value of the input utterance; And using the extracted emotion value of the speech as an input value of a long and short-term memory model (LSTM model), classifying the emotion of the speech in consideration of the change in emotion in the conversation in the messenger client based on the LSTM model includes The present invention can classify appropriate emotions by recognizing changes in emotions in conversations made in natural language.