Implementing Multinomial Naive Bayes for Employee Empowerment: A Transparent Platform for Task Management and Issue Resolution

Developing a positive motivational culture among employees in today's changing workplace is achieved through effective communication and problem-solving. This abstract introduces an innovative Employee Empowerment Platform designed to enhance transparency and collaboration within the work-force. Using a text classification algorithm, this work presents an employee authorization system that classifies and analyzes problem reports submitted by employees. Using a multi-step approach to data preprocessing, the system extracts HTML tags, square brackets, and special characters from text data collected from problem reports. Count Vectorization and TF-IDF are two mathematical techniques used for transforming preprocessed text into features. At its heart, the platform uses a polynomial naive base classification to identify positive and negative emotions in problem reports and assign them to the appropriate category. In addition, the system uses data visualization techniques to provide a comprehensive understanding of the model's performance. Through accuracy plots and confusion matrices, you can study the prediction skill of the classifier. By establishing a clear process for finding work and facilitating problem classification, our employee empowerment program helps companies identify common concerns about work and fosters an exchange of ideas between employees creating a workplace environment that nurtures individual growth and collective success. The main objective of the platform is to increase enthusiasm and empowerment in the workplace by addressing challenges quickly.