Authors : Manan Sharma

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/ytp8y4ut

Scribd : https://tinyurl.com/npfaxtyv

DOI : https://doi.org/10.38124/ijisrt/25apr943

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

Keywords : NLP, BERT, LSTM, Business Communication, Sentiment Analysis, Real-Time Language Translation.

References :

1. Cho, K., et al. (2014). Learning Phrase Representations Using RNN Encoder-Decoder for
   Statistical Machine Translation. arXiv preprint arXiv:1406.1078.
2. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
4. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
5. Howard, J., & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the Association for Computational Linguistics (ACL).
6. Kalusivalingam, S., & Kathiresan, S. (2021). Comparative Study of Transformer-Based and Recurrent-Based Models for Sentiment Analysis in Customer Feedback. Journal of Artificial Intelligence Research and Applications, 13(4), 44-55.
7. Lewis, M., Liu, Y., Goyal, N., et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of ACL.
8. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015). Learning to Diagnose with LSTM Recurrent Neural Networks. arXiv preprint arXiv:1511.03677.
9. McAuley, J., & Leskovec, J. (2013). Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. RecSys.
10. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.
11. Peters, M. E., et al. (2018). Deep Contextualized Word Representations. arXiv preprint arXiv:1802.05365.
12. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Technical Report.
13. Si, Z., Xie, P., & Yang, D. (2020). A Hybrid Approach to Contextual Embedding and Sequential Data Processing for NLP. Proceedings of the Association for Computational Linguistics (ACL).
14. Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer.
15. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.