Emotion　detection　systems　play　a　crucial　role　in　enhancing　human-computer　interaction.　Existing　systems　predominantly　rely　on　machine　learning　techniques.　This　study　introduces　a　novel　emotion　detection　method　that　employs　deep　learning　techniques　to　identify　five　basic　human　emotions　and　the　pleasure　dimensions　(valence)　associated　with　these　emotions,　using　text　and　keystroke　dynamics.　To　facilitate　this,　we　develop　a　non-acted　dataset,　DEKT-345　x　2,　which　includes　text　and　keystroke　features.　The　dataset　is　created　by　inducing　emotions　in　participants　under　controlled　conditions.　Deep　learning　models　are　subsequently　employed　to　predict　a　person＇s　affective　state　using　textual　content.　Semantic　analysis　of　the　text　data　is　achieved　by　employing　the　global　vector　(Glove)　representation　of　words.　For　both　text　and　keystroke-based　analysis,　one-dimensional　convolutional　neural　network　(Conv1D),　long　short-term　memory　(LSTM),　sandwich　Conv1D,　and　sandwich　LSTM　models　are　employed.　The　robustness　of　our　proposed　method　is　assessed　using　the　DEKT-345　x　2　dataset,　which　collects　text　and　keystroke　information　from　69　participants.　Through　parameter　tuning　on　training　and　validation　data,　we　establish　models　that　demonstrate　superior　performance　compared　to　five　related　approaches　and　three　machine　learning　classifiers.　Our　proposed　framework　achieves　an　accuracy　of　88.57%　using　the　LSTM　model,　80%　using　the　sandwich　LSTM　model,　71.42%　using　the　Conv1D　model,　and　51.48%　using　the　sandwich　Conv1D　model　on　text　data　across　the　five　emotion　classes.