摘要
首先使用CBOW算法对通过Python爬取的评论进行了高效且低维的向量表示,然后采用LSTM模型对这些向量进行训练。通过实验对比了LSTM模型与朴素贝叶斯、决策树、随机森林以及RNN在预测能力方面的表现,并提供了全面的模型比较。实验结果表明,LSTM模型的准确率更高,具有一定的适用性。
In this paper,the CBOW algorithm is first used tOmake efficient and low-dimensional vector representations of comments crawled by Python,and then these vectors are trained using the LSTM model.This paper alsOcompares the performance of the LSTM model with Naive Bayes,decision trees,random forests,and RNN in terms of predictive ability through experiments.The paper is rigorously structured.A comprehensive model comparison is provided.Experimental results show that the LSTM model has higher accuracy and has certain applicability.
作者
刘晏男
杨凯
董小刚
LIU Yannan;YANG Kai;DONG Xiaogang(School of Mathematics&Statistics,Changchun University of Technology,Changchun 130012,China)
出处
《长春工业大学学报》
CAS
2024年第3期233-240,共8页
Journal of Changchun University of Technology
基金
国家自然科学基金项目(11901053)
吉林省自然科学基金项目(20220101038JC)。
