Sentiment　is　a　high-level　abstraction,　and　it　is　a　challenging　task　to　accurately　extract　sentimental　features　from　visual　contents　due　to　the　＂affective　gap＂.　Previous　works　focus　on　extracting　more　concrete　sentimental　features　of　individual　objects　by　introducing　saliency　detection　or　instance　segmentation　into　their　models,　neglecting　the　interaction　among　objects.　Inspired　by　the　observation　that　interaction　among　objects　can　impact　the　sentiment　of　images,　we　propose　the　Sentiment　Interaction　Distillation　(SID)　Network,　which　utilizes　object　sentimental　interaction　to　guide　feature　learning.　Specifically,　we　first　utilize　a　panoptic　segmentation　method　to　obtain　objects　in　images;　then,　we　propose　a　sentiment-related　edge　generation　method　and　employ　Graph　Convolution　Network　to　aggregate　and　propagate　object　relation　representation.　In　addition,　we　propose　a　knowledge　distillation　framework　to　utilize　interaction　information　guiding　global　context　feature　learning,　which　can　avoid　noisy　features　introduced　by　error　propagation　and　a　varying　number　of　objects.　Experimental　results　show　that　our　method　outperforms　the　state-of-the-art　algorithm,　e.g.,　about　1.2%　improvement　on　the　Flickr　dataset　and　1.7%　on　the　most　challenging　subset　of　Twitter　I.　It　is　demonstrated　that　the　reasonable　use　of　interaction　features　can　improve　the　performance　of　sentiment　analysis.