Visual　sentiment　is　subjective　and　abstract,　and　it　is　very　challenging　to　locate　the　sentiment　features　from　images　accurately.　Some　researchers　devote　themselves　to　extracting　visual　features　but　ignore　the　relation　features.　However,　sentiment　reaction　is　a　comprehensive　action　of　visual　content,　and　regions　may　express　different　emotions　and　contribute　to　the　image　sentiment.　This　paper　takes　the　abstract　sentiment　relation　as　the　starting　point　and　proposes　the　Weakly　Supervised　Interaction　Discovery　Network　that　couples　detection　and　classification　branch.　Specifically,　the　first　branch　detects　sentiment　maps　with　the　cross-spatial　pooling　strategy,　which　generates　the　representations　of　emotions.　Then,　we　employ　a　stacked　Graph　Convolution　Network　to　extract　the　interaction　feature　from　the　above　features.　The　second　branch　utilizes　both　interaction　and　visual　features　for　robust　sentiment　classification.　Extensive　experiments　on　six　benchmark　datasets　demonstrate　that　the　proposed　method　exceeds　the　state-of-the-art　methods　for　image　sentiment　analysis.