Attention　mechanism　has　been　a　successful　method　for　multimodal　affective　analysis　in　recent　years.　Despite　the　advances,　several　significant　challenges　remain　in　fusing　language　and　its　nonverbal　context　information.　One　is　to　generate　sparse　attention　coefficients　associated　with　acoustic　and　visual　modalities,　which　helps　locate　critical　emotional　semantics.　The　other　is　fusing　complementary　cross-modal　representation　to　construct　optimal　salient　feature　combinations　of　multiple　modalities.　A　Conditional　Transformer　Fusion　Network　is　proposed　to　handle　these　problems.　Firstly,　the　authors　equip　the　transformer　module　with　CNN　layers　to　enhance　the　detection　of　subtle　signal　patterns　in　nonverbal　sequences.　Secondly,　sentiment　words　are　utilised　as　context　conditions　to　guide　the　computation　of　cross-modal　attention.　As　a　result,　the　located　nonverbal　features　are　not　only　salient　but　also　complementary　to　sentiment　words　directly.　Experimental　results　show　that　the　authors＇　method　achieves　state-of-the-art　performance　on　several　multimodal　affective　analysis　datasets.