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ID 118261
Author
Ren, Fuji The University of Tokushima|Hefei University of Technology Tokushima University Educator and Researcher Directory KAKEN Search Researchers
Keywords
word emotion classification
complex emotion
emotion intensity prediction
emotion-topic variation
hierarchical Bayesian network
Content Type
Journal Article
Description
In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word emotion information from text, and discover the distribution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, surprise, anxiety, sorrow, anger and hate. We use a hierarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without considering any complicated language features. Our experiment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
Journal Title
China Communications
ISSN
16735447
Publisher
China Communications Magazine
Volume
9
Issue
3
Start Page
99
End Page
109
Published Date
2012-03
EDB ID
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology