CBE : Corpus-Based of Emotion for Emotion Detection in Text Document

被引：0

作者：

Rachman, Fika Hastarita ^{[1
]}

Sarno, Riyanarto ^{[2
]}

Fatichah, Chastine ^{[2
]}

机构：

[1] Univ Trunojoyo Madura, Dept Informat, Bangkalan Madura, Indonesia

[2] Inst Teknol Sepuluh Nopember, Dept Informat, Surabaya, Indonesia

来源：

2016 3RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, COMPUTER, AND ELECTRICAL ENGINEERING (ICITACEE) | 2016年

关键词：

corpus of emotion; WNA; ANEW; Emotion Detection; Categorical model; Dimensional model; LDA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Emotion Detection is a part of Natural Language Processing (NLP) that still evolve. Emotional Corpus that had been widely used are Wordnet Affect Emotion (WNA) and ANEW (Affective Norms for English Words). There are two ways to analyze the text based Emotion Detection: Categorical and Dimensional Model. Each model has different advantages and disadvantages. And each model has a different concept to predict emotion. The contribution of this research is forming automatic emotional corpus with merging two computational model. It called Corpus-Based of Emotion (CBE). CBE developed from ANEW and WNA with term similarity measure and distance of node approach. Latent Dirichlet Allocation (LDA) is used too for automatically expand CBE. The CBE attributes are a score of Valence (V), Arousal (A), Dominance (D) and categorical label emotion. Categorical label emotion based on six basic emotion of Ekman. Based on experiment results, it is known that CBE is able to improve the accuracy in detection of emotions. F-Measure using WNA+ANEW is 0.50 and F-Measure using CBE with expanding is 0.61.

引用

页码：331 / 335

页数：5

共 15 条

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