Sadeque, Farig YousufAhmed, Md FaisalPrithila, Sara JerinElora, Kohinoor SultanaAhmed, Al RafiChy, Md. Shamsul Rahat2025-01-142025-01-14©20242024-10ID 24241137ID 21101147ID 21101092ID 24341123http://hdl.handle.net/10361/25149Cataloged from PDF version of thesis.Includes bibliographical references (pages 61-64).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.This research delves into the critical role of encryption in Natural Language Processing (NLP), emphasizing its significance as an emergency text platform, akin to a text-based emergency broadcast service. The study analyzes sentiments such as sadness, neutrality, worry, love, surprise, etc., utilizing a standard NLP pipeline for sentiment analysis. Additionally, it compares the results with an enhanced approach that incorporates an encryption module, aiming to quantify potential data loss in the latter scenario and highlighting the trade-offs between data protection and sentiment analysis accuracy in NLP. Addressing the prevalent absence of security components in existing NLP pipelines, this research introduces encryption to enhance security. This academic pursuit sheds light on the nuanced relationship between data protection and sentiment analysis accuracy in the context of NLP, providing valuable insights to guide the refinement of more resilient emergency text-based services while creating a cross-section between the NLP pipeline and Encryption Module.74 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.NLPNatural language processingMachine learningPolygraphic substitutionRNNLSTMGRU-Gaussian attention modelNatural language processing (Computer science).Sentiment analysis--Data processing.Data encryption (Computer science).Emotions--Computer simulation.Thesis