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.
Riahi, N., Safari, P. (2016). Implicit Emotion Detection from Text with Information Fusion. Journal of Advances in Computer Research, 7(2), 85-99.
MLA
Nooshin Riahi; Pegah Safari. "Implicit Emotion Detection from Text with Information Fusion". Journal of Advances in Computer Research, 7, 2, 2016, 85-99.
HARVARD
Riahi, N., Safari, P. (2016). 'Implicit Emotion Detection from Text with Information Fusion', Journal of Advances in Computer Research, 7(2), pp. 85-99.
VANCOUVER
Riahi, N., Safari, P. Implicit Emotion Detection from Text with Information Fusion. Journal of Advances in Computer Research, 2016; 7(2): 85-99.
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