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Ali Adil Qureshi¹, Maqsood Ahmad², Saleem Ullah¹

¹Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200 Pakistan.

Multimedia tools and applications

|June 26, 2023

概括

使用机器学习分析移动应用程序评论有助于改进应用程序. 将统计特征与TF-IDF结合起来,并使用支向量机器模型,在情绪分析中实现了最高的准确性.

科学领域:

自然语言处理自然语言处理.
机器学习机器学习
数据科学数据科学数据科学

背景情况:

移动应用程序越来越受欢迎,产生大量的用户评论.
分析这些审查对于应用程序的改进和开发至关重要,但由于它们的数量和复杂性,它们会带来挑战.
分类审查有助于用户选择合适的应用程序.

研究的目的:

提出一个框架来分析八个不同类别的移动应用程序评论的情绪分析.
评估机器学习模型与TF-IDF相结合的有效性,用于应用程序审查分析中的特征提取.

主要方法:

通过使用正规表达式和美丽的,删除了251,661条用户评论的数据集.
用于特征提取的使用术语频率-反向文档频率 (TF-IDF).
利用各种机器学习模型,包括支持矢量机器,并通过预处理和统计功能评估性能.

主要成果:

将统计特征与TF-IDF相结合,显著提高了模型性能.
支持矢量机模型在情绪分析中表现出最高的准确性.
这项研究为进一步研究提供了大量,多样化和平衡的数据集.

结论:

拟议的框架有效地分析了移动应用程序评论中的情绪.
结合TF-IDF和统计特征,以及SVM,为情绪分析提供了一个强大的方法.
这些发现可以指导研究人员选择应用程序审查分析的最佳模型,并有助于开发更好的移动应用程序.

关键词:

在谷歌的Play商店.移动应用程序评价移动应用程序评价人们的论挖掘.情绪分析是一种情绪分析.

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