Existing　image　aesthetics　assessment　methods　mainly　rely　on　the　visual　features　of　images　but　ignore　their　rich　semantics.　Nowadays,　with　the　widespread　application　of　social　media,　the　comments　corresponding　to　images　in　the　form　of　texts　can　be　easily　accessed　and　provide　rich　semantic　information,　which　can　be　utilized　to　effectively　complement　image　features.　This　paper　proposes　a　comment-guided　semantics-aware　image　aesthetics　assessment　method,　which　is　built　upon　a　multi-task　learning　framework　for　image　aesthetics　prediction　and　comment-guided　semantics　classification.　To　assist　image　aesthetics　assessment,　we　first　model　the　semantics　of　an　image　as　the　topic　features　of　its　corresponding　comments　using　Latent　Dirichlet　Allocation.　We　then　propose　a　two-stream　multitask　learning　framework　for　both　topic　feature　prediction　and　aesthetic　score　distribution　prediction.　Topic　feature　prediction　task　enables　to　infer　the　semantics　from　images,　since　the　comments　are　usually　unavailable　during　inference　and　comment-guided　semantics　can　only　serve　as　supervision　during　training.　We　further　propose　to　deeply　fuse　aesthetics　and　semantic　features　using　a　layerwise　feature　fusion　method.　Experimental　results　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　image　aesthetics　assessment　methods.