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Erschienen in: Artificial Intelligence Review 1/2018

27.01.2017

A Vietnamese adjective emotion dictionary based on exploitation of Vietnamese language characteristics

verfasst von: Vo Ngoc Phu, Vo Thi Ngoc Chau, Vo Thi Ngoc Tran, Nguyen Duy Dat

Erschienen in: Artificial Intelligence Review | Ausgabe 1/2018

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Abstract

Emotion classification is used in many commercial applications and research applications. The semantic classification models (or sentiment classification methods) are based on the vocabulary of the emotion dictionary being studied and being used very much to this day. In this study, a Vietnamese sentiment dictionary includes Vietnamese terms (Vietnamese nouns, Vietnamese verbs, Vietnamese adjectives, etc.) which the valences (and polarities) are calculated by using Ochiai measure through Google search engine and many Vietnamese adjective phrases which the valences (and polarities) are identified based on Vietnamese language characteristics. The Vietnamese adjectives often bear emotion which values (or semantic scores) are not fixed and are changed when they appear in different contexts of these phrases. Therefore, if the Vietnamese adjectives bring sentiment and their semantic values (or their sentiment scores) are not changed in any context, then the results of the emotion classification are not high accuracy. We propose many rules based on Vietnamese language characteristics to determine the emotional values of the Vietnamese adjective phrases bearing sentiment in specific contexts. Our Vietnamese sentiment adjective dictionary is widely used in applications and researches of the Vietnamese semantic classification.

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Metadaten
Titel
A Vietnamese adjective emotion dictionary based on exploitation of Vietnamese language characteristics
verfasst von
Vo Ngoc Phu
Vo Thi Ngoc Chau
Vo Thi Ngoc Tran
Nguyen Duy Dat
Publikationsdatum
27.01.2017
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 1/2018
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-017-9538-6

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