A Deep Learning Approach for Multilingual Sentiment Analysis

Abstract Sentiment analysis is pivotal for extracting insights from user-generated content across multilingual digital platforms. While traditional methods perform well in monolingual settings, they often struggle with the complexities of linguistic diversity, including syntactic variations, data scarcity in low-resource languages, and cross-lingual domain shifts. This study addresses these challenges by proposing a hybrid deep learning framework that synergizes the strengths of transformer-based models, sequential learning, and graph-based reasoning for robust multilingual sentiment analysis. Leveraging the Amazon Multilingual Review Dataset—spanning English, German, French, Spanish, Japanese, and Chinese—it evaluates standalone models (mBERT, BiLSTM, GNN) and novel hybrid architectures (BERT-BiLSTM, GNN-BERT) enhanced with attention mechanisms. In experiments demonstrate that hybrid models consistently outperform standalone approaches, with GNN-BERT achieving a 93.4% F1-score by effectively integrating contextual embeddings, sequential dependencies, and relational structures. Key contributions include: (1) a scalable framework for language-agnostic sentiment classification, (2) rigorous per-language evaluation revealing performance disparities (e.g., 92.2% F1 for English vs. 84.5% for Arabic), and (3) solutions for low-resource language challenges through cross-lingual transfer. The study also highlights the role of attention mechanisms in improving interpretability by identifying sentiment-relevant tokens. These advancements bridge critical gaps in multilingual NLP, offering practical applications in e-commerce and social media analytics. For future work, it outlines directions including zero-shot transfer learning, handling code-switching, and integrating multimodal sentiment analysis to further broaden real-world applicability.