Fine-Grained Emotion Analysis of Online Comments Based on the Fusion of Ontology and Deep Learning

To solve the problems of poor fine grained emotion recognition and poor interpretability of deep learning methods in previous studies, a fine-grained emotion analysis model integrating ontology and deep learning is proposed. The model uses domain ontology and convolutional neural networks fusion methods to identify explicit and implicit topics, and combines emotion dictionary and bidirectional long short-term memory network with attention model to identify fine-grained emotions of online comment texts. The experimental results show that the proposed fine-grained sentiment analysis method has advantages over other methods in accuracy, recall and F1 value. © 2023 Beijing University of Posts and Telecommunications. All rights reserved.