Chinese aspect level sentiment classification method based on pre-training sentiment embedding

Abstract

The invention discloses a Chinese aspect level sentiment classification method based on pre-training sentiment embedding, and provides a deep learning model combining pre-training sentiment embedding and semantic embedding. The model converts an aspect-level sentiment classification problem into a sentence pair classification problem, fine-tunes the BERT model on the word level of Chinese comments, and trains sentiment feature vectors on the word level by using an adversarial learning method and a double-layer bidirectional LSTM encoder module. And the obtained emotion information is dynamically combined with the last several layers of semantic information of BERT through linear weighting and a multi-head self-attention mechanism, so that the semantic and emotion information of a specific aspect category is obtained, and accurate emotion classification is carried out. The characteristics of Chinese patient comments are combined, rich decision information is provided for the patient, some doctors who pay more attention to performance and weaken medical science and ability are made to improve own work, and important guarantee is provided for improvement of online medical quality and doctor service level.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to an aspect-level emotion classification method for Chinese patient comments based on pre-trained emotion embedding. Background technique [0002] With the development of online medical service platforms, more and more patients choose to seek medical treatment online and are willing to share their feelings and experiences during the medical treatment process on the medical platform. Previous studies categorized the sentiment of patient reviews, which could determine whether patients were satisfied or dissatisfied with their physicians. However, one patient review may involve multiple aspects, and this classification is too general to distinguish the emotional polarity of multiple aspects in a Chinese patient review. For example, given the comment sentence "Dr. Lu's attitude is very cold, but after he performed the operation on me, my symptoms were significantly...

AI Technical Summary

Problems solved by technology

However, there are few facet-level sentiment classification methods for Chinese, and there is no related method research for patient reviews.

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