The　Internet＇s　development　has　prompted　social　media　to　become　an　essential　channel　for　disseminating　disaster-related　information.　Increasing　the　accuracy　of　emotional　polarity　recognition　in　tweets　is　conducive　to　the　government　or　rescue　organizations　understanding　the　public＇s　demands　and　responding　appropriately.　Existing　sentiment　analysis　models　have　some　limitations　of　applicability.　Therefore,　this　research　proposes　an　IDBO-CNN-BiLSTM　model　combining　the　swarm　intelligence　optimization　algorithm　and　deep　learning　methods.　First,　the　Dung　Beetle　Optimization　(DBO)　algorithm　is　improved　by　adopting　the　Latin　hypercube　sampling,　integrating　the　Osprey　Optimization　Algorithm　(OOA),　and　introducing　an　adaptive　Gaussian-Cauchy　mixture　mutation　disturbance.　The　improved　DBO　(IDBO)　algorithm　is　then　utilized　to　optimize　the　Convolutional　Neural　Network-Bidirectional　Long　Short-Term　Memory　(CNN-BiLSTM)　model＇s　hyperparameters.　Finally,　the　IDBO-CNN-BiLSTM　model　is　constructed　to　classify　the　emotional　tendencies　of　tweets　associated　with　the　Hurricane　Harvey　event.　The　empirical　analysis　indicates　that　the　proposed　model　achieves　an　accuracy　of　0.8033,　outperforming　other　single　and　hybrid　models.　In　contrast　with　the　GWO,　WOA,　and　DBO　algorithms,　the　accuracy　is　enhanced　by　2.89%,　2.82%,　and　2.72%,　respectively.　This　study　proves　that　the　IDBO-CNN-BiLSTM　model　can　be　applied　to　assist　emergency　decision-making　in　natural　disasters.