Application of Natural Language Processing Technology in Multilingual Sentiment Analysis and Sentiment Recognition

Authors

  • Yang Yuan School of Humanities and Communication, University of Sanya, Sanya 572022, Hainan, China

Abstract

The recent increase in social media data in multiple languages has led to fragmented emotional utterances that contain dialects, slang, and emojis. Traditional NLP (Natural Language Processing) models are ineffective in detecting cross-lingual implicit emotions; thus, there is a need to develop a robust multimodal multilingual emotion recognition framework. This paper introduces a multilingual emotion understanding framework. First, the study utilizes XLM-R for cross-lingual semantic alignment and employs parallel corpus comparison learning to ensure the aligned encoding of emotional utterances in different languages within the same space. Second, a multimodal emotion symbol fusion mechanism is employed, utilizing CLIP and Emoji2Vec, to better represent the semantic meaning of emojis and images. Third, fastText and a dynamic dictionary mechanism are applied to adapt to dialects and slang, which facilitates the recognition of emotional utterances and non-standard texts. Fourth, adversarial training as part of transfer learning is used to increase cross-lingual robustness and emotion consistency. Lastly, BERT-CRF combined with an attention mechanism is utilized for joint recognition of polarities and fine-grained emotions, leading to better detection of sarcasm and implicit emotional understanding. Experiments show that the model achieves an accuracy of 89.2% in English emotion recognition across five languages, and the accuracy of dialect recognition in high-resource dialects is improved to 90.2% through a dynamic mechanism. With an overall accuracy of 82% and attention coverage of 69.3%, the framework’s effectiveness in cross-language, multimodal, and non-standard texts was validated.

Keywords: Multilingual Emotion Recognition; Cross-lingual Semantic Alignment; Multimodal Emotion Fusion; Adversarial Transfer Training; Fine grained Emotion Classification

Cite As

Y. Yuan, "Application of Natural Language Processing Technology in Multilingual Sentiment Analysis and Sentiment Recognition", Engineering Intelligent Systems, vol. 34 no. 5, pp. 593-606, 2026.




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Published

2026-09-01

Issue

Vol. 34 No. 5 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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