Sentiment analysis in employee experience using natural language processing and machine learning

This chapter focuses on the use of sentiment analysis in the handling of employee experience. When organisations start thinking about the experience of their employees as a factor that influences performance and turnover, sentiment analysis provides a quantitative way of measuring emotions, satisfaction and engagement of employees. This chapter attentions on several NLP techniques that can be employed in the analysis of the employee feedback which include tokenization, stop- word removal and vectorization. It also looks at how other machine learning models such as Naive Bayes, Support Vector Machines, and Long Short- Term Memory can be used to categorize emotions as positive, negative, or neutral. In addition, the problem of language, culture, and data bias is described, and the ways to solve them are also described. The future potential of the real- time emotion analysis and the use of sentiment data along with the organizational KPIs for improving the management of employees' experience is depicted.