AI-Based Sentiment Analysis of Public Perception Under Bangabandhu Sheikh Mujibur Rahman - S.M. Ruhul Amin; Daljit Kaur Gill; Tapan Kumar Palit (International Journal of Algorithms Design and Analysis Review, 2025)
Sheikh Mujibur Rahman, known as Bangabandhu, played a pivotal role in Bangladesh’s post-liberation period (1971–1975). Understanding public sentiment during his leadership is crucial for historical analysis. This study employs Artificial Intelligence (AI)-based Sentiment Analysis to examine public perception through archived newspapers, parliamentary speeches, and historical records. Using Natural Language Processing (NLP) techniques, including sentiment classification and opinion mining, we analyze textual data to assess the prevailing public mood during his governance. Our methodology involves collecting textual data from newspapers, speeches, and government reports from 1972–1975. We preprocessed the data using tokenization, lemmatization, and stop-word removal, followed by sentiment classification using BERT-based transformers and Lexicon-based sentiment analysis. The findings reveal a dynamic shift in public sentiment, reflecting socio-political challenges such as economic reforms, famine, and political instability. The results provide insights into the public’s perception of Bangabandhu’s policies and governance, contributing to historical discourse and AI applications in political analysis. This study highlights the intersection of AI and historical research, showcasing how NLP can extract meaningful narratives from past events. The research also underscores the importance of preserving historical texts digitally for further computational analysis. Future research may extend to cross-country comparative sentiment studies to analyze leadership perceptions in post-colonial nations.
Keywords: AI in political analysis; Bangabandhu; Public Perception; natural language processing; sentiment analysis