A Corpus-based Multi-label Emotion Classification using Maximum Entropy

Ye Wu; Fuji Ren

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

A Corpus-based Multi-label Emotion Classification using Maximum Entropy

In Proceedings of the 6th International Workshop on Natural Language Processing and Cognitive Science - Volume 0ICEIS, 103-110, 2009 , Milan, Italy

Authors: Ye Wu and Fuji Ren

Affiliation: The University of Tokushima and Beijing University of Posts and Telecommunications, Japan

Abstract: Thanks to the Internet, blog posts online have emerged as a new grassroots medium, which create a huge resource of text-based emotion. Comparing to other ideal experimental settings, what we obtained by modeling blogs would be more private, honest and polemic because web logs from the World Wide Web evolve and respond more to real-world events. In this paper, our corpus consists of a collection of blog posts, which annotated as multi-label to make the classification of emotion more precise than the single-label set of basic emotions. Employing a maximum entropy classifier, our results show that the emotions can be clearly separated by the proposed method. Additionally, we show that the micro-average F1-score of multi-label detection increase when the mount of available training data further increases.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 13.58.39.23

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Wu, Y. and Ren, F. (2009). A Corpus-based Multi-label Emotion Classification using Maximum Entropy. In Proceedings of the 6th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2009) - NLPCS; ISBN 978-989-8111-92-0, SciTePress, pages 103-110. DOI: 10.5220/0002169901030110

@conference{nlpcs09,
author={Ye Wu. and Fuji Ren.},
title={A Corpus-based Multi-label Emotion Classification using Maximum Entropy},
booktitle={Proceedings of the 6th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2009) - NLPCS},
year={2009},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002169901030110},
isbn={978-989-8111-92-0},
}

TY - CONF

JO - Proceedings of the 6th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2009) - NLPCS
TI - A Corpus-based Multi-label Emotion Classification using Maximum Entropy
SN - 978-989-8111-92-0
AU - Wu, Y.
AU - Ren, F.
PY - 2009
SP - 103
EP - 110
DO - 10.5220/0002169901030110
PB - SciTePress