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基于深度学习的新能源汽车口碑评论情感分析

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研究汽车购买平台的评论能够有效地挖掘出消费者对新能源汽车的看法,进而引导市场消费,可以实现传统汽车向新能源汽车的良好过渡。论文提出一种针对评论文本的情感分类方法—BS2CLA,用情感词典对Bert转成的词向量进行情感词加权,使用双通道的CNN模型,结合BiLSTM-Attention。论文采集汽车之家真实的评论,在该基础上进行实验,结果表明,论文模型在F1、Accuracy和AUC三个指标上都优于基线模型。该模型可以更好鉴别消费者对某款新能源汽车的情感,促进市场更好更快发展。
Emotional Classification Algorithm of Comment Text Based on Emotion Dictionary and Deep Learning
The comments on automobile purchasing platforms can effectively dig out consumers'views on new energy vehicles.Also,it can guide the market consumption to achieve a good transition from traditional vehicles to new energy vehicles.In this pa-per,a sentiment classification method for comment text,BS2CLA,is proposed.The sentiment word vector converted by Bert is weighted by sentiment dictionary,and the two-channel CNN model is used in combination with Bilstm-attention.Real comments of autohome are climbed and experiments are carried out on this basis.The results show that the model in this paper is better than the baseline model in F1,Accuracy and AUC.This model can better identify the emotion of a new energy vehicle and promote better and faster development of the market.

sentiment analysisnew energy vehiclesemotion dictionarydouble carbon goaldeep learning

王晨悦、朱小栋

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上海理工大学管理学院 上海 200093

情感分析 新能源汽车 情感词典 双碳目标 深度学习

2024

计算机与数字工程
中国船舶重工集团公司第七0九研究所

计算机与数字工程

CSTPCD
影响因子:0.355
ISSN:1672-9722
年,卷(期):2024.52(11)