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Multi-label Emotion Detection via Emotion-Specified Feature Extraction and Emotion Correlation Learning
https://tokushima-u.repo.nii.ac.jp/records/2008322
https://tokushima-u.repo.nii.ac.jp/records/2008322f8a1cbbf-7797-4fe0-ac50-0ad1f074ad41
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
taffc_14_1_475.pdf (1.38 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||
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| 公開日 | 2020-11-09 | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版社版DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/TAFFC.2020.3034215 | |||||||||||||
| 言語 | ja | |||||||||||||
| 関連名称 | 10.1109/TAFFC.2020.3034215 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Multi-label Emotion Detection via Emotion-Specified Feature Extraction and Emotion Correlation Learning | |||||||||||||
| 言語 | en | |||||||||||||
| タイトル別表記 | ||||||||||||||
| その他のタイトル | MULTIPLE EMOTION DETECTION VIA EMOTION-SPECIFIED FEATURE EXTRACTION AND EMOTION CORRELATION LEARNING | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Deng, Jiawen
× Deng, Jiawen
× 任, 福継
WEKO
401
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Textual emotion detection is an attractive task while previous studies mainly focused on polarity or single-emotion classification. However, human expressions are complex, and multiple emotions often occur simultaneously with non-negligible emotion correlations. In this paper, a Multi-label Emotion Detection Architecture (MEDA) is proposed to detect all associated emotions expressed in a given piece of text. MEDA is mainly composed of two modules: Multi-Channel Emotion-Specified Feature Extractor (MC-ESFE) and Emotion Correlation Learner (ECorL). MEDA captures underlying emotion-specified features through MC-ESFE module in advance. MC-ESFE is composed of multiple channel-wise ESFE networks. Each channel is devoted to the feature extraction of a specified emotion from sentence-level to context-level through a hierarchical structure. Based on obtained features, emotion correlation learning is implemented through an emotion sequence predictor in ECorL. During model training, we define a new loss function, which is called multi-label focal loss. With this loss function, the model can focus more on misclassified positive-negative emotion pairs and improve the overall performance by balancing the prediction of positive and negative emotions. The evaluation of proposed MEDA architecture is carried out on emotional corpus: RenCECps and NLPCC2018 datasets. The experimental results indicate that the proposed method can achieve better performance than state-of-the-art methods in this task. | |||||||||||||
| 言語 | en | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Multi-label | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Emotion Detection | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Emotion Correlation | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Multi-label Focal Loss | |||||||||||||
| 書誌情報 |
en : IEEE Transactions on Affective Computing 巻 14, 号 1, p. 475-486, 発行日 2020-10-27 |
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| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 19493045 | |||||||||||||
| 出版者 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| 言語 | en | |||||||||||||
| 権利情報 | ||||||||||||||
| 言語 | en | |||||||||||||
| 権利情報 | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||
| EID | ||||||||||||||
| 識別子 | 371978 | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
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| 言語 | eng | |||||||||||||
