在餐厅评论中预测情绪极性,使用基于进化XGBoost的顺序回归方法
Dana A Al-Qudah1,2, Ala' M Al-Zoubi2, Alexandra I Cristea3
1King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
PeerJ. Computer science
|February 3, 2025
概括
本研究介绍了一种新的PSO-XGBoost模型,用于分析约旦食品行业的客户评论. 先进的情绪分析模型准确地分类阿拉伯语和英语反,改善业务洞察力.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 业务分析 业务分析
背景情况:
- 数字化转型需要对移动应用程序的多语言客户反进行高级分析.
- 约旦的食品和餐饮行业在理解在线评论方面面临着挑战.
- 情绪分析和机器学习为从文本数据中提取有价值的商业见解提供了机会.
研究的目的:
- 开发和评估一种新的情绪分析模型,用于对约旦食品和餐厅行业的客户评论进行分类.
- 解决自动文本分析和用户情绪状态识别方面的挑战.
- 为企业增强对阿拉伯语和英语客户反的理解.
主要方法:
- 利用情绪极性和生物识别技术进行全面的审查分析.
- 从Talabat应用程序收集并准备阿拉伯语和英语评论.
- 开发了一个四个阶段的模型:数据收集,准备,构建和评估.
- 提出了一个整数回归模型,将极端梯度提升 (XGBoost) 和粒子群集优化 (PSO) 结合起来,用于多语言预测.
主要成果:
- 拟议的PSO-XGBoost算法在与支持矢量机 (SVM) 和其他方法相比显示出更高的性能.
- 在英语和阿拉伯语数据集中实现较低的根平均平方误差 (RMSE).
- 具体来说,PSO-XGB模型在阿拉伯评论中产生了0.7722的RMSE,超过了PSO-SVM (0.9988).
结论:
- PSO-XGBoost模型提供了一种有效的方法,用于对多语言在线评论的情绪分析.
- 这项研究为企业提供了一个强大的工具,以更好地了解客户反和改进服务.
- 这些发现突显了先进机器学习技术在应对现实世界商业挑战方面的潜力.
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