The human-like emotions recognition using mutual information and semantic clues

Authors:
Hao-Chiang Koong Lin;Min-Chai Hsieh;Wei-Jhe Wang
Affiliations:
Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan;Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan;Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan
Venue:
Edutainment'11 Proceedings of the 6th international conference on E-learning and games, edutainment technologies
Year:
2011

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Abstract

In this work, we collect the sentences posted in Plurk as our corpus. The emoticons are classified into four types based on Thayer's 2-D Model which is composed of valence (positive/negative emotions) and arousal (the strength of emotions). The system will preprocess the sentence to eliminate the useless information, and then transform it to be the emotion lexicon. Besides, this research analyzes three kinds of semantic clues: negation, transition, and coordinating conjunctions. The final emotion is decided by SVM and the merging algorithm proposed in this work.