Background <p>Sentiment analysis is the approach used to identify the variation of emotional behavior of the software developer during the product development lifecycle.</p> Aim <p>Our research aims to ensure software quality by considering code complexity as a measuring parameter, which may have a positive or negative effect on the product.</p> Methodology <p>To check the model's performance, we have collected data from various real-time projects and predicted the amount and type of code smell. The developer's emotional score is evaluated using the SentiCR and Sentistrength tools.</p> Result <p>The Random Forest classifier achieved the highest accuracy of 96.3%, with a precision of 90.6%, recall of 89.2%, and F-measure of 88.4%. Long methods, large classes, comments and dead code are detected from the code snippets. Daily sentiment analysis signifies that on Monday, the developer is having positive emotions, which results in less amount of code smells.</p> Conclusion <p>This study concludes by quantifying the code smell detected during the sentiment analysis of developers. Sentiment analysis provides better correlation between emotion and software quality which is better than traditional approaches.</p>