A Comparative Analysis of Natural Language Processing Models: BERT and LSTM in Enhancing Business Communication

Natural Language Processing (NLP) has become an asset in business communication, enabling better understanding of human sentiments influencing corporate transactions. By breaking down the language barriers, it facilitates increased global collaboration at both macro as well as micro levels. In fact, decision makers at different levels of hierarchy are using NLP presently to distil relevant information and to identify key themes for concise and prompt action. The present study is aimed at the comparative analysis of NLP Models: Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) to assess their effectiveness for various AI-driven applications. This analysis is based on primary research and supported by secondary resources to provide a comprehensive evaluation. In the process, a suitable model was chosen to formulate modules like chatbots, real-time language translation services, customer support automation, etc. Further tools, like Sentiment Analysis systems, were developed to ensure accurate interpretation of clients’ responses. The findings indicate that BERT has an edge over LSTM in developing deeper contextual understanding for real-time language processing quintessential to gain actionable insights from inter-company communications, customer feedback, and documents due to its bidirectional processing capabilities.