基于主题建模和人工智能的在线员工评估工作满意度分析方法

A. Özdemir, Aytuğ Onan, Vildan ÇINARLI ERGENE
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引用次数: 0

摘要

在本研究中,通过对主题建模方法的使用进行基本的文献综述,并考虑员工的在线评价,在一些基本分类器上对所提出的样本选择方法的性能进行评估,以确定和分析工作满意度的因素。此外,为了有效地表示数据集,评估了不同表示结构的有效性,并在文本挖掘领域使用分类集成方法方面取得了主要成果。在这项工作中,强调了机器学习方法可以在分类方面实现高性能,并且可以有效地和可扩展地处理大型数据集。本研究使用的数据集来自www.kaggle.com。共有67529条来自谷歌、亚马逊、网飞、脸书、苹果和微软员工的评论被评估。在本研究的范围内,将开发一种基于人工智能的文本挖掘方法,并为人工智能方法的文本挖掘带来解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Topic Modelling and Artificial Intelligence Based Method Using Online Employee Assessments to Analyze Job Satisfaction
In this study, the performance of the proposed sample selection method was evaluated on some basic classifiers by conducting a basic literature review on the use of topic modelling methods by considering the online evaluations of the employees in order to determine and analyze the job satisfaction factors. In addition, the effectiveness of different representation structures are evaluated in order to represent the data sets effectively and the main results are obtained regarding the use of classification ensemble methods in the field of text mining. In this work it was emphasized that machine learning methods can achieve high performance in classification and work effectively and scalably with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple and Microsoft were evaluated. Within the scope of this study, a text mining and artificial intelligence-based method will be developed and a solution will be brought to text mining with artificial intelligence methods.
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