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

作　　者：张颖[1] ZHANG Ying(Network&Information Center,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学网络信息中心,江西南昌330013

出　　处：《软件导刊》2024年第12期53-57,共5页Software Guide

基　　金：江西省教育厅科技研究项目(GJJ191660)。

摘　　要：针对基于词向量的网络模型性能过分依赖分词准确性的问题,提出基于FastText字向量表示方法结合SelfAttention与BiLSTM的大学生情感分析方法(character-SATT-BiLSTM)。使用fasttext模型生成文本字向量,通过双向长短时记忆模型提取上下文语义特征,利用自注意力机制强化关键信息,最后使用Softmax分类器判断情感类别。实验结果显示,字向量文本表示方法比词向量更适合论坛文本情感分类,同时character-SATT-BiLSTM相比characterLSTM、character-BiLSTM等模型的效果更优,分类性能分别提高了6%和3%。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 using 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 performance 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 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]