# Multi-Modal Emotion Recognition for Online Education Using Emoji Prompts
## 5 Conclusions and Future Work  
In this study, we designed a multi-modal emotion analysis method for online education data to assess students' learning and evaluation. This method uses emoji usage to fuse text data in comments for emotion polarity classification. The biggest difference from the current multi-modal alignment methods is that we enhanced the ability to analyze emotional expressions through the prompt learning method using emoji data, especially for comment data where emotional characteristics were not prominent; the emoji prompt can effectively improve the accuracy of a sentiment analysis. Through comparative experiments, we established the feasibility of the model, and through ablation experiments, it was further proven that the fusion of emoji expression data can indeed improve the accuracy of emotion analysis. At the same time, the model also has some shortcomings. On the one hand, the module for integrating emoji data is too simplistic, relying solely on the simple matching of emoji data to obtain emotional polarity, which introduces significant errors. However, the model focuses more on textual features and uses a relatively simple and singular method for matching emoji data features, without designing more complex emotional feature models specifically for emoji data. This leads to a deficiency in the model's extraction of emotional features from emoji data, and the feature fusion does not achieve the best results, increasing errors. Therefore, our model can only use emojis for prompt learning.  
Our method integrates multi-modal features which can make use of emoji features to increase the dimension of emotion polarity, thus improving the accuracy of emotion analysis. However, our model relies solely on the label characteristics of the emojis and lacks the deep emotional features of the emojis, due to the low computational complexity of the direct matching method of emoji expressions and the lack of deep fusion of emotional features, thus needing to be enhanced in terms of model migration and robustness. In future work, according to the characteristics of the education platform we used, we will further enhance the enthusiasm of the students on the platform to participate, collect more comprehensive comment data, and use the large language model to enhance the data, so  
Figure 6: Ablation curves based on emoji data.  
as to improve the emotion analysis accuracy of the model and the migration ability of the platform, while further optimizing the emoji emotion module, particularly for modeling prompted emoji data. We will extract more refined emotional features and optimize the fusion algorithm. At the same time, we will expand the dimensions of the multi-modal data further, such as by incorporating multi-frame audio and video data, to enhance the model's robustness and applicability.  
The authors confirm their contribution to the paper as follows: study conception and design: X.Q. and J.L.; data collection: X.Q.; analysis and interpretation of results: J.L. and Y.Z.; draft manuscript preparation: X.Q. and J.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Guangxi Natural Science Foundation (No. 2022 GXNSF-BA035510), the Guangxi Key Research and Development Program (No. Guike AB23075178), the Guangxi Key Laboratory of Image and Graphic Intelligent Processing (No. GIIP207), and the National Natural Science Foundation of China (No. 62267002).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.