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Abstract：
Amid the wave of globalization, the phenomenon of cultural amalgamation has surged in frequency, bringing to the fore the heightened prominence of challenges inherent in cross-cultural communication. To address these challenges, contemporary research has shifted its focus to human-computer dialogue. Especially in the educational paradigm of human-computer dialogue, analysing emotion recognition in user dialogues is particularly important. Accurately identify and understand users\' emotional tendencies and the efficiency and experience of human-computer interaction and play. This study aims to improve the capability of language emotion recognition in human-computer dialogue. It proposes a hybrid model (BCBA) based on bidirectional encoder representations from transformers (BERT), convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), and the attention mechanism. This model leverages the BERT model to extract semantic and syntactic features from the text. Simultaneously, it integrates CNN and BiGRU networks to delve deeper into textual features, enhancing the model\'s proficiency in nuanced sentiment recognition. Furthermore, by introducing the attention mechanism, the model can assign different weights to words based on their emotional tendencies. This enables it to prioritize words with discernible emotional inclinations for more precise sentiment analysis. The BCBA model has achieved remarkable results in emotion recognition and classification tasks through experimental validation on two datasets. The model has significantly improved both accuracy and F1 scores, with an average accuracy of 0.84 and an average F1 score of 0.8. The confusion matrix analysis reveals a minimal classification error rate for this model. Additionally, as the number of iterations increases, the model\'s recall rate stabilizes at approximately 0.7. This accomplishment demonstrates the model\'s robust capabilities in semantic understanding and sentiment analysis and showcases its advantages in handling emotional characteristics in language expressions within a cross-cultural context. The BCBA model proposed in this study provides effective technical support for emotion recognition in human-computer dialogue, which is of great significance for building more intelligent and user-friendly human-computer interaction systems. In the future, we will continue to optimize the model\'s structure, improve its capability in handling complex emotions and cross-lingual emotion recognition, and explore applying the model to more practical scenarios to further promote the development and application of human-computer dialogue technology.
摘要：
在全球化的浪潮中，文化融合现象激增，突出强调跨文化交际中固有的挑战。为了应对这些挑战，当代研究已将重点转移到人机对话上。尤其是在人机对话的教育范式中,分析用户对话中的情感识别尤为重要。准确识别和理解用户的情感倾向以及人机交互和游戏的效率和体验。本研究旨在提高人机对话中的语言情感识别能力。它提出了一种基于来自变压器（BERT）的双向编码器表示的混合模型（BCBA），卷积神经网络(CNN)，双向门控递归单位(BiGRU)，注意机制。该模型利用BERT模型从文本中提取语义和句法特征。同时,它集成了CNN和BiGRU网络，以更深入地研究文本特征，增强模型在细致入微的情感识别方面的熟练程度。此外，通过引入注意力机制，该模型可以根据单词的情绪倾向为单词分配不同的权重。这使其能够优先考虑具有可辨别的情绪倾向的单词，以进行更精确的情绪分析。通过在两个数据集上的实验验证，BCBA模型在情感识别和分类任务中取得了显著的效果。该模型的准确性和F1得分都有了显著提高，平均准确率为0.84，平均F1评分为0.8。混淆矩阵分析揭示了该模型的最小分类错误率。此外,随着迭代次数的增加，模型的召回率稳定在约0.7。这一成就展示了该模型在语义理解和情感分析方面的强大功能，并展示了其在跨文化背景下处理语言表达中的情感特征方面的优势。本研究提出的BCBA模型为人机对话中的情感识别提供了有效的技术支持,这对于构建更加智能、人性化的人机交互系统具有重要意义。在未来,我们将继续优化模型的结构，提高其处理复杂情绪和跨语言情绪识别的能力，并探索将该模型应用于更多的实际场景，进一步促进人机对话技术的发展和应用。
