CLAS-Net: A study on cross-lingual intelligent sentiment analysis model fusing semantic alignment

Abstract

In the context of the deep integration of globalization and digitalization, the cross-lingual dissemination of news and public opinion information has become an increasingly significant challenge. This study proposes a novel cross-lingual sentiment analysis framework, CLAS-Net, designed to address the bottlenecks of current public opinion analysis systems in multilingual scenarios. The framework combines the cross-lingual contrastive learning capabilities of XLM-RoBERTa with the precise sentiment feature extraction ability of BiLSTM-Attention, enabling efficient analysis of multilingual public opinion. In monolingual tasks for English and Portuguese, CLAS-Net achieves accuracies of 92% and 89%, respectively, representing a 29 percentage point improvement compared to baseline models. In more challenging multilingual settings, CLAS-Net maintains a high accuracy of 83%, a 29 percentage point improvement over the baseline model. CLAS-Net (Cross-Lingual Alignment Sentiment Network) demonstrates strong adaptability and practical value when processing real-world social media and news data, providing reliable technical support for cross-lingual public opinion monitoring and analysis in the global context.