## 2 Materials and Methods
### Azure Cognitive Services Text Analytics  
The second component utilizes sentiment analysis to evaluate the emotional tone in written communications, such as emails, reports, or chat messages. By applying natural language processing (NLP) techniques, organizations interpret insights from employees' language, revealing their sentiments, attitudes, and potential stress levels.  
This analysis provides a range of benefits:  
* By analyzing text, organizations understand how employees feel about their work, projects, and workplace dynamics. This helps detect any underlying issues before they escalate.
* Understanding sentiment allows organizations to improve communication strategies, ensuring messages are received positively and reducing misunderstandings.
* By collecting and analyzing sentiment data over time, organizations track employee morale and engagement changes, allowing for informed decision-making regarding workplace policies and programs.  
In the framework of Industry 5.0, sentiment analysis supports a more responsive and adaptive organizational culture. By focusing on employees' emotional well-being through written expressions, organizations create a more supportive work environment that values psychological safety and fosters innovation.  
Figure 7 presents a screenshot of the Azure Text Analytics service configuration, showcasing the setup for sentiment analysis using Classify Text. The integration is made using the C# programming language.  
Sentiment analysis is integrated with other workplace tools and platforms to provide real-time feedback. This service is applied to internal chat systems, email exchanges, or project management software to continuously monitor the emotional climate of teams and departments. This real-time feedback enables managers to proactively address potential issues, fostering a more empathetic and supportive leadership style. The sentiment analysis is customized to detect specific emotional triggers or keywords indicating stress, frustration, or satisfaction, allowing for a tailored approach to employee well-being.  
The authors propose Equation (10) to compute an employee dissatisfaction score (\(DSC\)) on a scale from 1 to 10 using Azure Text Analytics for sentiment analysis. This formula calculates the number of negative messages related to the total number of messages.  
\[DSC_{i}=10-\frac{\sum_{k=1}^{n}DS_{k}}{n}\times 10 \tag{10}\]  
where  
\(DSC_{i}\) is the dissatisfaction score component, which quantifies employee dissatisfaction based on sentiment analysis results for day \(i\);  
\(DS_{k}\) has a value of 1 for each negative text message \(k\);  
\(n\) is the number of messages sent each day by the employee.  
Azure Text Analytics, as illustrated by the setup in Figure 7, is a powerful tool for organizations looking to align emotional well-being with business goals. Through advanced NLP techniques, companies create a more emotionally intelligent work environment where employees' mental and emotional health is as valued as their productivity.  
Two thousand tests were conducted to evaluate the service's accuracy and assess its suitability for integration in such a task. The results of these tests are presented in the Results section.