Shared　information　refers　to　the　common　semantic　information　across　multiple　modalities,　and　complementary　information　refers　to　modality-specific　information　that　complements　other　modalities.　How　to　fully　utilize　this　information　is　a　key　issue　in　the　multimodal　sentiment　analysis.　In　this　paper,　we　first　propose　a　Slice　Aggregation　(SA)　algorithm　to　address　the　issue　of　correlation　over　time.　We　use　sliding　windows　to　calculate　the　horizontal　and　vertical　correlations,　and　then　aggregate　slices　into　a　series　of　chunks,　each　represents　a　set　of　successive　slices　with　consistent　correlation.　Second,　we　introduce　a　Dynamic　Fusion　(DF)　strategy　comprising　two　components:　shared　information　fusion　and　complementary　information　fusion.　The　former　utilizes　a　multilayer　perceptron　(MLP)　to　extract　high-level　shared　representations,　whereas　the　latter　employs　a　cross-modal　multi-head　attention　mechanism　to　fuse　low-level　complementary　information.　Finally,　we　propose　an　SA-DF　framework　where　SA　organizes　raw　slices　into　correlation-consistent　chunks,　and　DF　progressively　fuses　features　across　these　chunks.　The　concatenated　fused　features　are　used　for　final　sentiment　prediction.　The　experiments　on　CMU-MOSI　and　CH-SIMS　datasets　show　that　the　proposed　SA-DF　can　achieve　the　best　performance　on　sentiment　analysis　tasks　when　compared　with　the　state-of-the-art　baselines.　©　China　Computer　Federation　(CCF)　2025.