Abstract

This study explores the application of deep learning-based natural language processing technologies in multilingual sentiment analysis. By examining the performance of deep learning models such as BERT and LSTM in multilingual settings, the research demonstrates the effectiveness of these models in cross-linguistic sentiment classification tasks. Despite progress, major challenges in multilingual sentiment analysis include language and cultural differences, insufficient complex context processing, and data imbalance. Future research directions include optimizing the models' contextual understanding abilities, leveraging multilingual data resources, exploring novel neural network architectures, and improving assessment metrics. With these measures, the accuracy and efficiency of multilingual sentiment analysis are expected to be significantly enhanced, further advancing the global application of natural language processing technologies.