With　the　popularity　of　online　opinion　expressing,　automatic　sentiment　analysis　of　images　has　gained　considerable　attention.　Most　methods　focus　on　effectively　extracting　the　sentimental　features　of　images,　such　as　enhancing　local　features　through　saliency　detection　or　instance　segmentation　tools.　However,　as　a　high-level　abstraction,　the　sentiment　is　difficult　to　accurately　capture　with　the　visual　element　because　of　the　＂affective　gap＂.　Previous　works　have　overlooked　the　contribution　of　the　interaction　among　objects　to　the　image　sentiment.　We　aim　to　utilize　interactive　characteristics　of　objects　in　the　sentimental　space,　inspired　by　human　sentimental　principles　that　each　object　contributes　to　the　sentiment.　To　achieve　this　goal,　we　propose　a　framework　to　leverage　the　sentimental　interaction　characteristic　based　on　a　Graph　Convolutional　Network　(GCN).　We　first　utilize　an　off-the-shelf　tool　to　recognize　objects　and　build　a　graph　over　them.　Visual　features　represent　nodes,　and　the　emotional　distances　between　objects　act　as　edges.　Then,　we　employ　GCNs　to　obtain　the　interaction　features　among　objects,　which　are　fused　with　the　CNN　output　of　the　whole　image　to　predict　the　final　results.　Experimental　results　show　that　our　method　exceeds　the　state-of-the-art　algorithm.　Demonstrating　that　the　rational　use　of　interaction　features　can　improve　performance　for　sentiment　analysis.