基于情感分析的高校招聘模型

Felix Uloko, Raphael Ozighor Enihe, Clinton Immunhierokene Obrorindo
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引用次数: 0

摘要

大学的传统角色是教学和研究,以发展社会为目标,通过培养有技能和受过良好教育的毕业生,为国家经济发展做出积极贡献。然而,这些高等院校的招聘过于依赖候选人简历提供的资格,而忽视了他们从研究活动和在线出版物中获得的合适性。本研究确定了高等院校招聘趋势的见解,并使用人工智能来产生更全面和平衡的决策过程,以满足资格和适用性。该方法采用机器学习过程,使用多项式Naïve贝叶斯来训练模型,并使用维德情绪分析仪来进行准确性和测试。所使用的数据集包含Resume实例以及作者发布信息。结果显示,该模型的得分为83.9%,情感分析得分为1,表明总体得分为正。结果表明,情感分析可以帮助教育机构改进招聘模式,吸引更多合适的候选人担任这些角色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Sentiment Analysis Based Model for Recruitment by Higher Institutions
The traditional roles of a university are teaching and research with the aim of developing society and contributing positively to the national economic development by producing skilled and well-tutored graduates. However, recruitments by these higher institutions are too reliant on the eligibility provided by Resumes of candidates, while neglecting their suitability drawn from their research activity and publications online. This study identifies insights in recruitment trends in higher institutions of learning and uses Artificial Intelligence to produce a more rounded and balanced decision-making process that caters for both eligibility and suitability. The methodology employs the machine learning process using the Multinomial Naïve Bayes for training the model as well as the Vader sentiment analyzer for accuracy and testing. The datasets used contained Resume instances as well as author publication information. The results show a score of 83.9% for the model as well as a sentiment analysis score of 1, indicating an overall positive score. The results show that sentiment analysis can help educational institutions in improving their recruitment models and attracting more suitable candidates for such roles.
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