Sentiment analysis during political crises is difficult because language, context, and public reaction can change rapidly. This challenge is especially underexplored for low-resource languages such as Bangla. We introduce **UnrestSent200K**, a Bangla crisis sentiment dataset containing approximately 200K Facebook and YouTube comments from the July–August 2024 Bangladesh uprising. The dataset covers five event-aligned phases, ranging from early escalation and internet blackout to regime transition and a later flood crisis. Each comment is linked to its parent post, allowing evaluation both with and without discourse context. All comments are annotated through a fully human process involving 14 native Bangla-speaking annotators and senior validation. The annotation process achieves substantial agreement, with Cohen’s kappa of 0.73, Krippendorff’s alpha of 0.71, and 94.2% blind-audit agreement. We benchmark fine-tuned encoders, prompted LLMs, and LoRA-tuned LLMs. Results show that adding parent-post context consistently improves performance, while temporal shifts across crisis phases cause large performance drops. Strong LLMs perform well, but they still struggle with sarcasm, implicit political references, and meanings that depend on the event phase. UnrestSent200K provides a benchmark for studying context-aware and temporally robust sentiment analysis in low-resource crisis discourse.