Author(s)

Ruiyang Qiao ¹, Xinpeng Huang ¹

Affiliation(s)

¹ South China Normal Unviersity, Guangzhou, Guangdong, China

Corresponding Author

Ruiyang Qiao

ABSTRACT

Natural language processing and sentiment analysis are important research areas in the field of artificial intelligence. With the development of globalization, cross-lingual sentiment analysis has become a challenging task. This paper focuses on the application of deep learning in natural language processing for cross-lingual sentiment analysis. Firstly, an overview of natural language processing and sentiment analysis is provided, including their definitions and development history. Then, the challenges in cross-lingual sentiment analysis are discussed, including the influence of language and cultural differences on sentiment identification, as well as the issues in data annotation and cross-lingual data. Next, the application of deep learning in natural language processing and sentiment analysis is highlighted, covering the principles of deep learning algorithms, text representation and feature extraction methods, and application cases in sentiment analysis. Furthermore, a deep learning approach for cross-lingual sentiment analysis is proposed, presenting the task definition, datasets, models, and evaluation metrics in detail. Finally, through experimental results and analysis, the performance of the cross-lingual sentiment analysis models is evaluated, and the advantages, limitations, and future directions of deep learning methods in this field are discussed.

KEYWORDS

Natural language processing, sentiment analysis, cross-lingual sentiment analysis, deep learning

CITE THIS PAPER

Ruiyang Qiao, Xinpeng Huang, Application of Deep Learning in Cross-Lingual Sentiment Analysis for Natural Language Processing. Journal of Artificial Intelligence Practice (2024) Vol. 7: 1-6. DOI: http://dx.doi.org/10.23977/jaip.2024.070101.

REFERENCES

[1] Sun, K. (2023). Rational Sentiment Human-Machine Negotiation Based on Pre-trained Language Models. Dissertation, Guangxi Normal University.
[2] Kang, G. (2023). Research on Cross-Lingual Sentiment Classification based on Bilingual Word Embedding Models. Dissertation, Hebei University of Technology.
[3] Wang, X. (2022). Research on Cross-Lingual Sentiment Analysis Method based on Pre-trained Models [Dissertation, Guangdong University of Foreign Studies.

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