摘要
随着大数据时代的到来,深度学习在情感分析领域备受关注。针对传统情感分析方法处理海量、高维、非结构化数据时的效率和精度问题,本研究提出了一种创新的大数据情感分析框架。该框架融合了词向量表示、卷积神经网络(Convolutional Neural Networks,CNN)、循环神经网络和注意力机制等技术,构建了端到端的情感分析系统。本研究在电商评论、酒店评论和Twitter等大规模数据集上进行了实验,准确率和F1值均超95%,显著优于现有方法。本研究推进了深度学习在情感分析领域的创新应用,为社交媒体监测、用户体验分析、舆情研究等场景提供了技术支持,具有重要理论价值和实践意义。
With the arrival of the big data era,deep learning has attracted much attention in the field of sentiment analysis.Aiming at the efficiency and accuracy problems of traditional sentiment analysis methods when dealing with massive,high-dimensional and unstructured data,this study proposes an innovative framework for big data sentiment analysis.The framework integrates techniques such as word vector representation,convolutional neural networks(CNN),recurrent neural networks,and attention mechanisms to construct an end-to-end sentiment analysis system.This study conducted experiments on large-scale datasets such as e-commerce reviews,hotel reviews,and Twitter,and the accuracy and F1 value are over 95%,which are significantly better than existing methods.This study advances the innovative application of deep learning in the field of sentiment analysis,provides technical support for social media monitoring,user experience analysis,public opinion research and other scenarios,and has important theoretical value and practical significance.
作者
凌芝拓
LING Zhituo(China Mobile Communications Group Guangdong Co.,Ltd.,Guangzhou Guangdong 510630,China)
出处
《信息与电脑》
2024年第19期163-165,共3页
Information & Computer
关键词
大数据
情感分析
深度学习
应用设计
big data
sentiment analysis
deep learning
application design
