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Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification

Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification

P Mohamed Shakeel, S Baskar
Copyright: © 2020 |Volume: 16 |Issue: 1 |Pages: 11
ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781799802761|DOI: 10.4018/IJTHI.2020010107
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MLA

Shakeel, P Mohamed, and S Baskar. "Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification." IJTHI vol.16, no.1 2020: pp.94-104. http://doi.org/10.4018/IJTHI.2020010107

APA

Shakeel, P. M. & Baskar, S. (2020). Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification. International Journal of Technology and Human Interaction (IJTHI), 16(1), 94-104. http://doi.org/10.4018/IJTHI.2020010107

Chicago

Shakeel, P Mohamed, and S Baskar. "Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification," International Journal of Technology and Human Interaction (IJTHI) 16, no.1: 94-104. http://doi.org/10.4018/IJTHI.2020010107

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Abstract

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.

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