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Authors: Kazuyuki Matsumoto ; Minoru Yoshida ; Kenji Kita and Fuji Ren

Affiliation: Tokushima University, Japan

ISBN: 978-989-758-158-8

Keyword(s): Emotion Corpus, Emotion Estimation, Bilingual Tagged Corpus.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Acquisition ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

Abstract: Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. We propose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As the result of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation incr eased up to 66.7% in Naive Bayes. 1 INTRODUCTION Recently, there have been many researches on emotion estimation from text in the field of sentiment analysis or opinion mining (Ren, 2009), (Ren and Quan, 2015), (Ren and Wu, 2013), (Quan and Ren, 2010), (Quan and Ren, 2014), (Ren and Matsumoto, 2015) and many of them adopted machine learning methods that used words as a feature. When the type of the target sentence for emotion estimation and the type of the sentence prepared as training data are different, as in the case of terminology in the problem of domain adaptation for document classification, the appearance tendency of the emotion words differs. This causes a problem in fluctuation of accuracy. On the other hand, when a word is used as a feature for emotion estimation, the sentence structure does not have to be considered. As a result, it is easy to apply the method to other languages. Only if we prepare a large number of corpora with annotation of emotion tags on each sentence, emotion would be easily estimated by using the machine learning method. In the machine learning method, because manual definition of a rule is not necessary, we can reduce costs to apply the method to other languages. However, just like the problem in the domain, depending on the (More)

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Paper citation in several formats:
Matsumoto, K.; Yoshida, M.; Kita, K. and Ren, F. (2015). An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language.In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 74-83. DOI: 10.5220/0005602200740083

@conference{keod15,
author={Kazuyuki Matsumoto. and Minoru Yoshida. and Kenji Kita. and Fuji Ren.},
title={An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={74-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005602200740083},
isbn={978-989-758-158-8},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language
SN - 978-989-758-158-8
AU - Matsumoto, K.
AU - Yoshida, M.
AU - Kita, K.
AU - Ren, F.
PY - 2015
SP - 74
EP - 83
DO - 10.5220/0005602200740083

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