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Information Journal Paper

Title

IMPLICIT EMOTION DETECTION FROM TEXT WITH INFORMATION FUSION

Pages

  85-99

Abstract

 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.

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    APA: Copy

    RIAHI, NOOSHIN, & SAFARI, PEGAH. (2016). IMPLICIT EMOTION DETECTION FROM TEXT WITH INFORMATION FUSION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH, 7(2 (24)), 85-99. SID. https://sid.ir/paper/328833/en

    Vancouver: Copy

    RIAHI NOOSHIN, SAFARI PEGAH. IMPLICIT EMOTION DETECTION FROM TEXT WITH INFORMATION FUSION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH[Internet]. 2016;7(2 (24)):85-99. Available from: https://sid.ir/paper/328833/en

    IEEE: Copy

    NOOSHIN RIAHI, and PEGAH SAFARI, “IMPLICIT EMOTION DETECTION FROM TEXT WITH INFORMATION FUSION,” JOURNAL OF ADVANCES IN COMPUTER RESEARCH, vol. 7, no. 2 (24), pp. 85–99, 2016, [Online]. Available: https://sid.ir/paper/328833/en

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