The　modern　computing　applications　are　presently　adapting　to　the　convenient　availability　of　huge　and　diverse　data　for　making　their　pattern　recognition　methods　smarter.　Identification　of　dominant　emotion　solely　based　on　the　text　data　generated　by　humans　is　essential　for　the　modern　human-computer　interaction.　This　work　presents　a　multimodal　text-keystrokes　dataset　and　associated　learning　methods　for　the　identification　of　human　emotions　hidden　in　small　text.　For　this,　a　text-keystrokes　data　of　69　participants　is　collected　in　multiple　scenarios.　Stimuli　are　induced　through　videos　in　a　controlled　environment.　After　the　stimuli　induction,　participants　write　their　reviews　about　the　given　scenario　in　an　unguided　manner.　Afterward,　keystroke　and　in-text　features　are　extracted　from　the　dataset.　These　are　used　with　an　assortment　of　learning　methods　to　identify　emotion　hidden　in　the　short　text.　An　accuracy　of　86.95%　is　achieved　by　fusing　text　and　keystroke　features.　Whereas,　100%　accuracy　is　obtained　for　pleasure-displeasure　classes　of　emotions　using　the　fusion　of　keystroke/text　features,　tree-based　feature　selection　method,　and　support　vector　machine　classifier.　The　present　work　is　also　compared　with　four　state-of-the-art　techniques　for　the　same　task,　where　the　results　suggest　that　the　present　proposal　performs　better　in　terms　of　accuracy.