|Table of Contents|

An Emotion Feature Extraction Method of BiLSTM-CNN based on Orthogonal Projection(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年01期
Page:
139-148
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
An Emotion Feature Extraction Method of BiLSTM-CNN based on Orthogonal Projection
Author(s):
Wei Subo1; 2; Zhang Shunxiang1; 2; Zhu Guangli1; 2; Sun Zhengyan1; 2; Li Jian1; 2
(1.School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China)
(2.Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei 230000, China)
Keywords:
emotional classification;  orthogonal projection;  BiLSTM;  CNN
PACS:
TP391.1
DOI:
10.3969/j.issn.1001-4616.2023.01.018
Abstract:
The emotion feature extraction method of BiLSTM-CNN based on orthogonal projection aims to obtain the weighted neutral word vector from the text,obtain the emotion feature with higher discrimination,and provide strong technical support for text emotion classification. The traditional deep learning model ignores the special meaning of words in the key local context information,resulting in the insufficient acquisition of emotional features. To solve this problem,a BiLSTM-CNN emotion feature extraction method based on orthogonal projection has been proposed. Firstly,the neutral word vector is projected into the orthogonal space of emotional polarity words,and the weighted neutral word vector is obtained. At the same time,the CNN deep learning model is used to extract the key semantics of the text. Then,the BiLSTM-Attention model and weighted neutral word vector are used to learn the semantic features of sentence emotion from the extracted key semantics,which makes the text more discriminative in sentiment classification. The experimental results show that the proposed sentiment feature extraction method can obtain more complete sentiment features,thus significantly improving the accuracy of text sentiment classification.

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Last Update: 2023-03-15