揭露欺骗:一个CNN和自适应的PSO方法来检测假的在线评论
1Department of C.S.E.Gitam IT, GITAM University, Visakhapatnam, Andhra Pradesh 530045 India.
概括
这项研究引入了一种使用卷积神经网络 (CNN) 和自然语言处理来检测虚假在线评论的新方法,达到99.4%的准确性. 这种方法提高了电子商务的可信度,并有助于消费者做出明智的决策.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 电子商务分析 电子商务分析
背景情况:
- 在线评论显著影响消费者行为和企业声誉.
- 假评的蔓延破坏了信任,并使数据分析复杂化.
- 现有的方法很难准确地识别欺骗性的在线反.
研究的目的:
- 开发一种准确和有效的方法来检测假的在线评论.
- 提高电子商务平台的可信度和消费者反.
- 为分析用户生成内容提供一个强大的工具.
主要方法:
- 这是一种多模式方法,将卷积神经网络 (CNN) 与自然语言处理 (NLP) 结合起来.
- 使用特征选择和高级文本表示技术 (例如BERT,word2vec,GloVe).
- 采用自适应粒子群集优化用于模型增强.
主要成果:
- 在假评论检测中达到99.4%的高准确率.
- 在精度,回忆和F-measure方面表现优于传统的机器学习分类器.
- 在多个数据集中展示了优越的概括性,并降低了计算复杂性.
结论:
- 拟议的基于CNN的模型有效地识别了虚假的在线评论.
- 这种方法显著提高了在线审查系统的可靠性.
- 为消费者,企业和电子商务平台提供实际好处.
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