Exploring cross-cultural communication content adaptability through advanced natural language processing and sentiment analysis

In the context of accelerated globalization, cross-regional cultural information dissemination has become the norm, but the interpretation of the same information by different cultural groups is significantly different, which often leads to communication failure, misunderstanding and even conflict. To this end, this study integrates natural language processing (NLP) and sentiment analysis technologies to propose innovative solutions: by constructing a multilingual text corpus covering news, social media, literature and other genres, it can completely restore the characteristics of global language use; The deep learning model is used to train the corpus to achieve accurate recognition of multilingual emotional tendencies, metaphorical expressions and culturally specific vocabulary. On this basis, automatic translation and content adjustment tools are developed, and on the premise of retaining the semantics of the original text, the symbol replacement (avoiding negative associations) and tone adaptation (conforming to local expression habits) are fine-tuned for the target culture. After field testing in multicultural areas and a large number of feedback data collection, the results showed that the acceptance of the optimized cross-cultural content in the target culture was significantly improved—the positive emotional feedback rate increased by 32 % on average, and the cultural misreading rate decreased by 19 %. This empirical evidence fully validates the great potential of NLP and sentiment analysis technology in enhancing the adaptability of cross-cultural communication.