{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T16:16:31Z","timestamp":1789143391719,"version":"build-2803163510"},"reference-count":23,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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\u2013computer dialogue. Especially in the educational paradigm of human\u2013computer dialogue, analysing emotion recognition in user dialogues is particularly important. Accurately identify and understand users\u2019 emotional tendencies and the efficiency and experience of human\u2013computer interaction and play. This study aims to improve the capability of language emotion recognition in human\u2013computer 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\u2019s 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\u2019s recall rate stabilizes at approximately 0.7. This accomplishment demonstrates the model\u2019s 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\u2013computer dialogue, which is of great significance for building more intelligent and user-friendly human\u2013computer interaction systems. In the future, we will continue to optimize the model\u2019s 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\u2013computer dialogue technology.<\/jats:p>","DOI":"10.7717\/peerj-cs.2166","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:05:36Z","timestamp":1719990336000},"page":"e2166","source":"Crossref","is-referenced-by-count":12,"title":["Pedagogical sentiment analysis based on the BERT-CNN-BiGRU-attention model in the context of intercultural communication barriers"],"prefix":"10.7717","volume":"10","author":[{"given":"Xin","family":"Bi","sequence":"first","affiliation":[{"name":"School of Literature, Heilongjiang University, Harbin, Heilongjiang, 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