This　paper　contributes　a　new,　real-world　web　image　dataset　for　cross-media　retrieval　called　FB5K.　The　proposed　FB5K　dataset　contains　the　following　attributes:　(1)　5130　images　crawled　from　Facebook;　(2)　images　that　are　categorized　according　to　users’　feelings;　(3)　images　independent　of　text　and　language　rather　than　using　feelings　for　search.　Furthermore,　we　propose　a　novel　approach　through　the　use　of　Optical　Character　Recognition　(OCR)　and　explicit　incorporation　of　high-level　semantic　information.　We　comprehensively　compute　the　performance　of　four　different　subspace-learning　methods　and　three　modified　versions　of　the　Correspondence　Auto　Encoder　(Corr-AE),　alongside　numerous　text　features　and　similarity　measurements　comparing　Wikipedia,　Flickr30k　and　FB5K.　To　check　the　characteristics　of　FB5K,　we　propose　a　semantic-based　cross-media　retrieval　method.　To　accomplish　cross-media　retrieval,　we　introduced　a　new　similarity　measurement　in　the　embedded　space,　which　significantly　improved　system　performance　compared　with　the　conventional　Euclidean　distance.　Our　experimental　results　demonstrated　the　efficiency　of　the　proposed　retrieval　method　on　three　different　public　datasets.　©　2019,　Springer　Nature　Switzerland　AG.