Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts
Abstract
:1. Introduction
- In multi-modal sentiment recognition, we used emoji emoticons as prompts, rather than relying solely on text data.
- We studied the effectiveness of emoji emoticons as features in multi-modal pre-training.
2. Related Works
3. The Proposed Method
3.1. Educational Data Collection and Pre-Processing
3.2. Multi-Modal Emotion Analysis Method
4. Experimental Results and Analyses
4.1. Datasets and Baseline Methods
4.2. Experiments and Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Division | Positive | Negative | Neutral |
---|---|---|---|---|
Restaurant | Training | 2164 | 807 | 637 |
Testing | 727 | 196 | 196 | |
Laptop | Training | 976 | 851 | 455 |
Testing | 337 | 128 | 167 | |
Training | 1507 | 1528 | 3016 | |
Testing | 172 | 169 | 336 |
Positive | ||||
老铁/old friend | 暴富/parvenu | 么么哒/like | 666/amazing | 233/laugh |
漂亮/beautiful | 忠诚/devotion | 干净/clean | 加油/come on | 实惠/substantial |
友善/friendly | 优秀/excellent | 欢喜/joyful | 权威/authoritative | 舒服/comfortable |
健康/healthy | 天使/angel | 安静/calm | 专心/concentrate | 准确/accurate |
完美/perfect | 容易/easy | 完整/intact | 昌盛/prosperity | 雄心/ambition |
和平/peace | 活力/vigor | 坚定/firm | 亮点/highlight | 赚/earn |
幸福/happiness | 新/new | 勤奋/industrious | 开通/accomplish | 稳了/confirmed |
诚信/faithful | 文明/civilized | 出名/famous | 真实/real | 成熟/mature |
聪明/clever | 积极/active | 精英/elite | 捷报/good news | 保险/assure |
Negative | ||||
凉了/washed-up | 傻逼/sucker | 垃圾/rubbish | 智障/mentally retarded | 滚/scat |
毒/poison | 暴力/violence | 非法/illegality | 心机/craftiness | 虚假/sham |
嘈杂/noisy | 漏洞/leak | 事故/accident | 亏/deficit | 变态/abnormal |
浪费/waste | 花样/trick | 失败/failure | 冲突/conflict | 陈旧/obsolete |
妒忌/jealous | 谣言/rumor | 病人/patient | 恶势力/vicious power | 残/incomplete |
色情/erotic | 淫秽/bawdry | 错误/error | 失误/mistake | 流氓/immoral |
疯狂/insane | 缺少/lack | 敌对/hostility | 脆弱/weak | 蠢/foolish |
黑客/hacker | 陷阱/trap | 脏/dirty | 不合格/unqualified | 不安/uneasy |
诈骗/cheat | 自负/conceited | 丑陋/ugly | 恶意/malevolence | 烦恼/upset |
Model | Restaurant | Laptop | ||||
---|---|---|---|---|---|---|
Accuracy | F1 | Accuracy | F1 | Accuracy | F1 | |
ATAE-LSTM (Wang et al., 2016) [31] | 0.785 | 0.598 | 0.753 | 0.629 | 0.842 | 0.629 |
GTRU (Xue et al., 2018) [32] | 0.776 | 0.574 | 0.753 | 0.627 | 0.847 | 0.609 |
AA-LSTM (Xing et al., 2019) [35] | 0.777 | 0.586 | 0.762 | 0.631 | 0.856 | 0.611 |
CapsNet (Jiang et al., 2019) [36] | 0.781 | 0.616 | 0.747 | 0.618 | 0.838 | 0.614 |
AS-Capsules (Wang et al., 2019) [37] | 0.793 | 0.619 | 0.752 | 0.621 | 0.845 | 0.625 |
GIN (Yin et al., 2020) [38] | 0.812 | 0.624 | 0.759 | 0.632 | 0.871 | 0.650 |
MIMLLN (Li et al., 2020) [39] | 0.783 | 0.606 | 0.753 | 0.614 | 0.858 | 0.635 |
BERT (Devlin et al., 2018) [40] | 0.824 | 0.644 | 0.816 | 0.662 | 0.886 | 0.736 |
GIN-BERT (Yin et al., 2020) [38] | 0.840 | 0.660 | 0.830 | 0.653 | 0.895 | 0.749 |
MIMLLN-BERT (Li et al., 2020) [39] | 0.828 | 0.651 | 0.830 | 0.624 | 0.881 | 0.731 |
Hier-GCN-BERT (Cai et al., 2020) [41] | 0.843 | 0.663 | 0.833 | 0.658 | 0.897 | 0.746 |
Ours | 0.856 | 0.682 | 0.848 | 0.684 | 0.912 | 0.785 |
Model | Restaurant | Laptop | ||||
---|---|---|---|---|---|---|
Accuracy | F1 | Accuracy | F1 | Accuracy | F1 | |
Seed word prompt (Ours) | 0.856 | 0.682 | 0.848 | 0.684 | 0.912 | 0.785 |
No feed words (Ours) | 0.031↓ | 0.026↓ | 0.033↓ | 0.031↓ | 0.035↓ | 0.032↓ |
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Qin, X.; Zhou, Y.; Li, J. Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts. Appl. Sci. 2024, 14, 5146. https://doi.org/10.3390/app14125146
Qin X, Zhou Y, Li J. Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts. Applied Sciences. 2024; 14(12):5146. https://doi.org/10.3390/app14125146
Chicago/Turabian StyleQin, Xingguo, Ya Zhou, and Jun Li. 2024. "Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts" Applied Sciences 14, no. 12: 5146. https://doi.org/10.3390/app14125146
APA StyleQin, X., Zhou, Y., & Li, J. (2024). Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts. Applied Sciences, 14(12), 5146. https://doi.org/10.3390/app14125146