Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly.
We introduce UNRESTSENT200K, a Bangla crisis sentiment dataset with approximately 200K Facebook and YouTube comments from the July-August 2024 Bangladesh uprising.
The dataset covers five event-aligned phases, from early escalation and internet blackout to regime transition and a later flood crisis.
Each comment is linked to its parent post, enabling evaluation with and without discourse context.
All comments are annotated through a fully human process involving 14 native Bangla-speaking annotators and senior validation, achieving substantial agreement (kappa = 0.73, alpha = 0.71) and 94.2% blind-audit agreement.
We benchmark fine-tuned encoders, prompted LLMs, and LoRA-tuned LLMs.
Results show that parent-post context consistently improves performance, while temporal shift across phases causes large performance drops.
Strong LLMs perform well, but still struggle with sarcasm, implicit political references, and phase-dependent meaning.
UNRESTSENT200K provides a benchmark for studying context-aware and temporally robust sentiment analysis in low-resource crisis discourse.
UNRESTSENT200K is available at https://sami0055.github.io/UNRESTSENT200K/