Emotion　identification　from　text　data　has　recently　gained　focus　of　the　research　community.　This　has　multiple　utilities　in　an　assortment　of　domains.　Many　times,　the　original　text　is　written　in　a　different　language　and　the　end-user　translates　it　to　her　native　language　using　online　utilities.　Therefore,　this　paper　presents　a　framework　to　detect　emotions　on　translated　text　data　in　four　different　languages.　The　source　language　is　English,　whereas　the　four　target　languages　include　Chinese,　French,　German,　and　Spanish.　Computational　intelligence　(CI)　techniques　are　applied　to　extract　features,　dimensionality　reduction,　and　classification　of　data　into　five　basic　classes　of　emotions.　Results　show　that　when　English　text　is　translated　to　French,　classification　accuracy　is　higher　than　others,　i.e.,　99.04%.　Whereas,　when　the　same　is　translated　to　Chinese　language,　its　detection　rate　is　lowest　among　target　languages.　It　is　concluded　that　emotions　remain　preserved　after　translation　to　some　extent.　Framework　consists　of　TFIDF　features.　PCA　and　Discriminant　Analysis　perform　good　to　detect　emotions　from　translated　data.