软件导刊2024，Vol.23Issue(12)：53-57,5.DOI:10.11907/rjdk.232208
基于Self-Attention与Bi-LSTM的大学生情感倾向研究
Research on Emotional Tendencies of College Students Based on Self-Attention and Bi-LSTM
摘要
Abstract
The performance of the network model based on the word vector relies heavily on the accuracy of word segmentation,a method of sentiment analysis for college students based on FastText character vector combined with Self-Attention and BiLSTM is proposed.Firstly,character vectors are generated using the fasttext model,then contextual semantic features are extracted by the bidirectional long and short-term memory model and key information is strengthened using the Self-Attention mechanism,finally,the sentiment categories are judged us-ing the Softmax classifier.The experimental results show that character vector is more suitable for short text than word vector,and character-SATT-BiLSTM has achieved better classification results than character-LSTM,character-BiLSTM and other models.The classification perfor-mance can be increased by 6%and 3%,respectively.关键词
FastText/字向量/双向长短时记忆/自注意力/情感倾向分析Key words
FastText/character vector/bidirectional long short-term memory/self-attention/emotional tendency analysis分类
信息技术与安全科学引用本文复制引用
张颖..基于Self-Attention与Bi-LSTM的大学生情感倾向研究[J].软件导刊,2024,23(12):53-57,5.基金项目
江西省教育厅科技研究项目(GJJ191660) （GJJ191660）
