文本审查和应用程序评级中的矛盾:使用文本特征进行预测和转移学习
Turki Aljrees1, Muhammad Umer2, Oumaima Saidani3
1College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
PeerJ. Computer science
|January 10, 2024
概括
这项研究使用用户评论和ELMo转移学习模型预测了真实的移动应用程序评级. 它准确地分析应用程序反,克服数值评级中的偏见.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 软件工程 软件工程 软件工程
背景情况:
- 像Google Play这样的移动应用商店是应用程序分发和用户反的关键平台.
- 用户评论提供了有价值的见解,但可能会受到偏见,与数字评级产生差异.
- 现有的方法很难将数值评级与用户评论中的丰富信息相协调.
研究的目的:
- 开发一种转移学习模型,根据用户评论预测移动应用程序的真实数值评分.
- 通过利用用户提供的文本评论的内容来解决偏见数字评级的问题.
- 评估拟议的ELMo模型与其他转移学习和机器学习方法的有效性.
主要方法:
- 采用了基于词向量表示的ELMo模型的转移学习方法.
- 从谷歌Play商店收集了应用程序评论和评级的数据集,涵盖14个应用程序类别.
- 使用TextBlob分析来分离有偏见和无偏见的用户评分,以确定评估的基本真相.
主要成果:
- 通过分析用户评论,ELMo模型展示了通过分析用户评论来预测真实数值评级的高潜力.
- 拟议的ELMo模型在准确性方面超过了其他三种转移学习和五种机器学习模型.
- 该研究成功地确定并解决了数字评级和用户报告的体验之间的差异.
结论:
- 转移学习,特别是使用ELMo模型,可以有效地从用户评论中预测准确的移动应用程序评分.
- 分析用户评论提供了比单纯的数字评分更真实的应用程序质量衡量标准.
- 这种方法为开发人员和用户提供了一个有价值的工具,可以更好地了解应用程序的性能和用户满意度.
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