The employees, as stakeholders of the organization, can contribute to the development and productiveness of the organization. In regards to satisfaction/dissatisfaction, the opinion of employees can perform dual rule. Firstly, it supports the organization to plan future strategies and enhance their yield; secondly, it can be helpful for aspirants in seeking their best choice. In this concern, we have classified the reviews of employees using two different modules. In the first module, we have experimented on ratings of reviews; then in the second module, the textual part of the reviews is used to classify employees as satisfied/unsatisfied. After that, the reasonable outcomes of both approaches are unified for the final prediction of reported reviews as proper/improper. For this purpose, we have implemented a purely supervised machine learning approach. The performance of state of the art classifiers along with TF-IDF (Term frequency-Inverse document frequency) and BoW (Bag-of word) is analyzed in the text module. In this comparison, ETC (Extra tree classifier) performed best in terms of accuracy in both modules. It shows 100% accuracy with rating and 79% accuracy with the textual part. Ultimately, we have implemented AND gate for the evaluation of proper/improper reviews. The results of AND gate evaluate that 76% of the reviews of employees are reported as properly and 24% are reported as improperly in the used dataset.

员工作为组织的利益相关者，可以为组织的发展和生产力做出贡献。关于满意度/不满意感，员工的意见可以执行双重规则。首先，它支持组织计划未来的战略并提高其收益；其次，它对有抱负的人寻求最佳选择会有所帮助。为此，我们使用两个不同的模块对员工的评价进行了分类。在第一个模块中，我们对评论评分进行了实验；然后在第二个模块中，评论的文本部分用于将员工分类为满意/不满意。此后，将两种方法的合理结果统一起来，以对所报告评论的最终预测进行适当/不正确的预测。以此目的，我们已经实施了纯监督的机器学习方法。在文本模块中分析了最先进的分类器的性能以及TF-IDF（术语频率-文档反向频率）和BoW（单词袋）。在此比较中，就两个模块的准确性而言，ETC（额外树分类器）表现最佳。它显示了100％的准确度和文字部分的79％的准确度。最终，我们实施了AND门来评估适当/不当的评论。AND gate的结果评估为，在使用的数据集中，对员工的评论的76％被正确地报告，而24％的不正确被报告。就两个模块的准确性而言，ETC（额外树分类器）表现最佳。等级显示为100％准确度，文字部分显示为79％准确度。最终，我们实施了AND门来评估适当/不当的评论。AND gate的结果评估为，在所使用的数据集中，对员工的评论的76％被正确地报告，而24％的报告被不正确地报告。就两个模块的准确性而言，ETC（额外树分类器）表现最佳。它显示了100％的准确度和文字部分的79％的准确度。最终，我们实施了AND门来评估适当/不当的评论。AND gate的结果评估为，在所使用的数据集中，对员工的评论的76％被正确地报告，而24％的报告被不正确地报告。