Ontology-Driven Affective Chinese Text Analysis and Evaluation Method

Authors:
Linhong Xu;Hongfei Lin
Affiliations:
Department of Computer Science and Engineering, Dalian University of Technology, Dalian,116024, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian,116024, China
Venue:
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Year:
2007

Citing 3
Cited 2

Visualizing the affective structure of a text document

CHI '03 Extended Abstracts on Human Factors in Computing Systems
Emotion estimation and reasoning based on affective textual interaction

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Affect analysis of text using fuzzy semantic typing

IEEE Transactions on Fuzzy Systems

Formalized Conflicts Detection Based on the Analysis of Multiple Emails: An Approach Combining Statistics and Ontologies

OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Construction and application of chinese emotional corpus

CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper studies text affective analysis from three different levels: words, sentences and discourses. Firstly, affective lexicon ontology is constructed by employing the manual and automatic classification methods referred to the present emotional classification. Then affective analysis of sentences and discourses is completed by emotional information of affective lexicon ontology. For sentential recognition, the lexical emotion information and semantic features are appended into Condition Random Fields, and the emotional chain of a text document is generated. Finally affective structure of a text document is evaluated by two different methods as single sentence evaluation and joint sentence evaluation. In analyzing the discourses, affective tone is acquired through Emotional Largest Chain method and Support Vector Machine method.