Deep Learning for Workplace Communication Analysis and Improvement

This article explores the possibility of deep learning applied to improving workplace communication. A comprehensive dataset was compiled, consisting of 10 thousand emails, 5 thousand meeting transcripts and 7 thousand instant messages derived from a corporate environment. We used Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTMs) to process and analyze this data. The models performed well, yielding accuracy of 88 % for CNN and higher at 91 % each for RNN and LSTM. Sentiment, keyword frequency and conversation topics were considered key pillars of the dynamics in communications. Based on these understandings, specific action plans were begun that resulted in an increase of 20 % clarity for communications and a rise of 25 % efficiency in team cooperation. The increases came from the adoption of pre-established indicators to measure improvement quantitatively. Deep learning can help in the dissection and improvement of workplace communication, as this study shows. The results show that communication efficiency can indeed be greatly improved, but they also point out the need to overcome challenges such as data privacy and AI algorithms. This study also indicates new avenues for integrating AI in organizational communication and provides information that can help to advance workplace interactions and effectiveness.