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What is Text analytics?

Textual analytics, also referred to as text analytics, is a method that utilizes natural language processing (NLP) and machine learning algorithms to extract valuable insights and patterns from unstructured textual data. When applied to employee feedback, textual analytics enables organizations to gain a deeper understanding of the sentiments and opinions expressed by their employees across various feedback channels, including engagement surveys, social media, and employee reviews.

By employing textual analytics, organizations can analyze employee feedback on various topics such as job satisfaction, performance, communication, and leadership. This analysis helps identify common themes and issues, such as employee discontentment with specific job aspects or company culture.

Furthermore, textual analytics facilitates sentiment identification, allowing organizations to gauge positive or negative attitudes toward particular topics or issues. By comprehending the sentiment expressed by employees, organizations gain insights into the emotional impact of different policies and initiatives on their workforce.

Overall, using textual analytics empowers organizations to make data-driven decisions and enhance the employee experience by identifying areas that require improvement and measuring the effectiveness of interventions.