When a Nation Speaks: Machine Learning and NLP in People's Sentiment Analysis During Bangladesh's 2024 Mass Uprising
NeutralArtificial Intelligence
- The study on sentiment analysis during Bangladesh's 2024 mass uprising highlights the emotional dynamics of civil unrest, focusing on public sentiments categorized as Outrage, Hope, and Despair. Utilizing a dataset of 2,028 annotated news headlines from Facebook, the research employed Latent Dirichlet Allocation (LDA) to identify themes such as political corruption and the impact of internet blackouts on sentiment patterns.
- This development is significant as it pioneers the application of natural language processing (NLP) in understanding the emotional landscape during a national crisis, providing insights into public sentiment that can inform policymakers and social scientists about the underlying issues driving civil unrest.
- The findings resonate with ongoing discussions in the field of NLP regarding the importance of language-specific sentiment analysis, particularly in low-resource languages like Bangla. This research contributes to a growing body of work aimed at enhancing machine learning applications in diverse linguistic contexts, while also addressing challenges such as hate speech classification and the need for robust datasets for effective sentiment analysis.
— via World Pulse Now AI Editorial System
