基于在线评论数据挖掘分析的求职软件优化研究

Optimization of Job-Hunting App Based on Online Review Data Mining

在互联网高速发展的背景下,在线求职已成为求职的主要渠道之一。为优化求职软件,基于文本挖掘和数据分析技术,选取下载量最高的6个求职软件从情感分析和分词及词频统计分类的角度进行研究,结果显示,界面设计、消息提示、个性化推荐、职位质量及用户隐私等方面是求职者对求职软件评价的关键因素。建议通过完善App的操作界面、加快信息的更新速度、加入用户个性化推荐、提高企业职位信息的安全性等措施对求职软件进行优化。

With the rapid development of the Internet and big data era,online job-hunting has become the main channel for people to apply for jobs.In order to optimize each job-hunting software,based on text mining and data analysis,six job-hunting apps with the most download volume are studied from the perspective of emotion analysis,word segmentation and word frequency statistical classification.The results show that interface design,message prompts,personalized recommendations,quality of position and user privacy are the key factors for job applicants to evaluate job-hunting software.And job-hunting software can be optimized by improving the operation interface of the App,accelerating the update of information,adding user personalized recommendations and improving the security of corporate job information.