An emotion recognition mechanism based on the combination of mutual information and semantic clues

Recently, text-based emotion recognition has become more and more highly regarded. After exploring the related research, we have found that: (1) In the semantics-based solution, the newly emerging compound words cannot be handled. (2) In the statistics-based solution, the emotional values are comput...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2012-03, Vol.3 (1), p.19-29
Main Authors: Lin, Hao-Chiang Koong, Hsieh, Min-Chai, Loh, Li-Chen, Wang, Cheng-Hung
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arousal
Artificial Intelligence
Blogs
Computational Intelligence
Emotion recognition
Emotions
Engineering
Motorcycles
Original Research
Random variables
Research methodology
Robotics and Automation
Semantics
Sentences
Statistical analysis
Support vector machines
Syntax
Trends
Two dimensional models
User Interfaces and Human Computer Interaction
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Summary:Recently, text-based emotion recognition has become more and more highly regarded. After exploring the related research, we have found that: (1) In the semantics-based solution, the newly emerging compound words cannot be handled. (2) In the statistics-based solution, the emotional values are computed only via the linear text clues; it lacks syntactic and semantic structural information. Therefore, in our effort to overcome the two concerns above, in the system presented here we construct an extensible lexicon and use semantic clues to analyze the sentences. In this work, we collected the sentences posted to the Plurk website 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 pre-process the sentence to eliminate the useless information, and then transform it to an emotion lexicon. Additionally, 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. Moreover, the recognition rate is promoted from 40.3 to 68.15% after the emotion lexicon is improved, and the semantic structural information is applied. The experimental results show that the work is promising.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-011-0086-7