一个基于支持向量回归模型的消费者情绪分析系统
1Changchun University of Science and Technology, School of Economics and Management, Changchun, Jilin, China.
PeerJ. Computer science
|June 22, 2023
概括
使用优化的支持向量回归 (SVR) 模型分析在线健身消费者评论,可以揭示关键偏好. 这种情绪分析预测购买意图,增强用户体验和商业战略.
科学领域:
- 消费者行为分析 消费者行为分析
- 计算语言学 计算语言学
- 机器学习应用 机器学习应用
背景情况:
- 消费者购买决策受到各种习惯和经验的影响.
- 在线平台需要了解各种消费者偏好的方法.
- 分析在线评论提供了对产品特性和情绪的见解.
研究的目的:
- 为在线健身平台开发消费者情绪分析系统.
- 根据情绪分析预测消费者支付意愿.
- 提高在线健身平台的服务质量和营销策略.
主要方法:
- 使用一个优化的支向量回归 (SVR) 模型.
- 采用区域卷积神经网络 (RCNN) 来进行特征提取.
- 用提取的消费者情绪特征训练SVR模型.
主要成果:
- 经过RCNN优化的SVR模型在情绪分析中显示出更高的准确性.
- 准确的情绪分析有助于企业有针对性的推广和销售增长.
- 增强的分析有助于用户有效地识别首选的健身项目.
结论:
- 开发的消费者情绪分析系统具有显著的实际价值.
- 改进的情绪分析对在线健身市场的企业和消费者都有好处.
- 这种方法为优化在线健身产品提供了可操作的见解.
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