Classification and Analysis of Employee Feedback with Deep Learning Algorithms

This study aims to enhance organizational processes and support decision-making for managers by conducting an automated analysis of employee feedback through text classification. Employee satisfaction and motivation are critical factors that directly impact sustainability and efficiency goals. To overcome the challenges of manual feedback analysis, the study employs Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The dataset comprises feedback collected from meeting notes, internal surveys, and manager-employee interviews, with data synthesis and preprocessing steps including text cleaning, tokenization, and modelling. The study's findings reveal that the CNN algorithm achieved the best performance, with an accuracy of 99.12%, a test loss of 0.0609, precision of 0.9912, recall of 0.9912, and an F1 score of 0.9911. This research demonstrates the valuable contribution of automated classification models in effectively and efficiently analysing employee feedback.