implicit emotion detection from text with information fusion

in this paper we have proposed an approach for emotion detection in implicit texts. we have introduced a combinational system based on three subsystems. each one analyzes input data from a different aspect and produces an emotion label as output. the first subsystem is a machine learning method. the second one is a statistical approach based on vector space model (vsm) and the last one is a keyword-based subsystem with an information fusion component to aggregate the final output of main system. we analyzed the performance of our proposed system on isear dataset with seven emotions: anger, joy, sad, shame, fear, disgust and guilt. the results show that our combinational system outperforms each subsystem with overall f-measure of 0.68 and at least up to 0.71 in terms of f1 in emotion level except for anger. the overall performance of the main system is 9.13% better than machine learning module, 16.6% better than vsm and 23% better than keyword-based.