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

Ye Wu, Fuji Ren

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.

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Paper Citation


in Harvard Style

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 - Volume 1: NLPCS, (ICEIS 2009) ISBN 978-989-8111-92-0, pages 103-110. DOI: 10.5220/0002169901030110


in Bibtex Style

@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 - Volume 1: NLPCS, (ICEIS 2009)},
year={2009},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002169901030110},
isbn={978-989-8111-92-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Workshop on Natural Language Processing and Cognitive Science - Volume 1: NLPCS, (ICEIS 2009)
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