识别重要的特征和机器学习技术,预测有帮助的评论
Shah Jafor Sadeek Quaderi1, Kasturi Dewi Varathan1
1Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
识别有用的在线评论对于消费者来说至关重要. 这项研究发现,语言,元数据,可读性,主观性和极性特征,当与随机森林一起使用时,可以准确地预测帮助力,达到89.36%的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 消费者行为 消费者行为
背景情况:
- 消费者在购买决策时严重依赖在线评论.
- 在线评论的压倒性数量需要确定最有帮助的评论.
- 现有的研究还没有充分利用可用的特征来预测评论的有用性.
研究的目的:
- 识别有意义的特征来预测有帮助的在线评论.
- 为了比较不同的机器学习模型在预测审查有用性的表现.
- 通过过有价值的评论来增强消费者决策过程.
主要方法:
- 使用语言,元数据,可读性,主观性和极性特征.
- 将五个机器学习模型应用于两个大型亚马逊开放数据集.
- 为了其预测能力,采用了随机森林技术.
主要成果:
- 随机森林模型使用已识别的显著特征,达到89.36%的准确性.
- 这一性能超过了研究中评估的其他机器学习技术.
- 在语言,元数据,可读性,主观性和极性等类别中确定了有助于有用性的关键特征.
结论:
- 拟议的功能集和随机森林模型有效地预测有帮助的在线评论.
- 这种方法可以大大帮助消费者浏览产品评论信息.
- 进一步的研究可以探索额外的特征和模型,以便在帮助性预测中获得更高的准确性.
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