WEKO3
アイテム
Research on Textual Emotion Recognition based on Deep Learning Methods
https://tokushima-u.repo.nii.ac.jp/records/2008940
https://tokushima-u.repo.nii.ac.jp/records/2008940a9ee81d1-0430-403e-bf46-04f75763bb68
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
k3520_abstract.pdf (78.7 KB)
|
|
|
|
k3520_review.pdf (39.4 KB)
|
|
|
|
k3520_fulltext.pdf (2.96 MB)
|
|
| Item type | 文献 / Documents(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2021-05-27 | |||||||||
| 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_db06 | |||||||||
| 資源タイプ | doctoral thesis | |||||||||
| 出版タイプ | ||||||||||
| 出版タイプ | NA | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||||||
| タイトル | ||||||||||
| タイトル | Research on Textual Emotion Recognition based on Deep Learning Methods | |||||||||
| 言語 | en | |||||||||
| タイトル別表記 | ||||||||||
| その他のタイトル | 深層学習に基づくテキスト感情分析に関する研究 | |||||||||
| 言語 | ja | |||||||||
| 著者 |
邓, 佳文
× 邓, 佳文
|
|||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Textual emotion recognition (TER) is the process of automatically identifying emotional states in textual expressions. It is a more in-depth analysis than sentiment analysis. Owing to its significant academic and commercial potential, TER has become an essential topic in the field of NLP. Over the past few years, although considerable progress has been conducted in TER, there are still some difficulties and challenges because of the nature of human emotion complexity. This thesis explores emotional information by incorporating external knowledge, learning emotion correlation, and building effective TER architectures. The main contributions of this thesis are summarized as follows: (1) To make up for the limitation of imbalanced training data, this thesis proposes a multi-stream neural network that incorporates background knowledge for text classification. To better fuse background knowledge into the basal network, different fusion strategies are employed among multi-streams. The experimental results demonstrate that, as the knowledge supplement, the background knowledge-based features can make up for the information neglected or absented in basal text classification network, especially for imbalance corpus. (2) To realize contextual emotion learning, this thesis proposes a hierarchical network with label embedding. This network hierarchically encodes the given sentence based on its contextual information. Besides, an auxiliary label embedding matrix is trained for emotion correlation learning with an assembled training objective, contributing to final emotion correlation-based prediction. The experimental results show that the proposed method contributes to emotional feature learning and contextual emotion recognition. (3) To realize multi-label emotion recognition and emotion correlation learning, this thesis proposed a Multiple-label Emotion Detection Architecture (MEDA). MEDA comprises two modules: Multi-Channel Emotion-Specified Feature Extractor (MC-ESFE) and Emotion Correlation Learner (ECorL). MEDA captures underlying emotion-specified features with MC-ESFE module in advance. With underlying features, emotion correlation learning is implemented through an emotion sequence predicter in ECorL module. Furthermore, to incorporate emotion correlation information into model training, multi-label focal loss is proposed for multi-label learning. The proposed model achieved satisfactory performance and outperformed state-of-the-art models on both RenCECps and NLPCC2018 datasets, demonstrating the effectiveness of the proposed method for multi-label emotion detection. |
|||||||||
| 言語 | en | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Textual Emotion Recognition | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Deep learning | |||||||||
| 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 | Data Imbalance | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Contextual Learning | |||||||||
| 書誌情報 |
発行日 2021-03-23 |
|||||||||
| 備考 | ||||||||||
| 言語 | ja | |||||||||
| 値 | 内容要旨・審査要旨・論文本文の公開 学位授与者所属 : 徳島大学大学院先端技術科学教育部（システム創生工学専攻） |
|||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 報告番号 | ||||||||||
| 学位授与番号 | 甲第3520号 | |||||||||
| 学位記番号 | ||||||||||
| 言語 | ja | |||||||||
| 値 | 甲先第400号 | |||||||||
| 学位授与年月日 | ||||||||||
| 学位授与年月日 | 2021-03-23 | |||||||||
| 学位名 | ||||||||||
| 言語 | ja | |||||||||
| 学位名 | 博士（工学） | |||||||||
| 学位授与機関 | ||||||||||
| 言語 | ja | |||||||||
| 学位授与機関名 | 徳島大学 | |||||||||
