Language Service in Cross-Border Agricultural E-Commerce: A Sentiment Analysis of Consumer Reviews Based on NLP

With the rapid development of cross-border agricultural e-commerce, consumer reviews have become a critical data source reflecting product quality and service experience. This study focuses on consumer reviews of agricultural products on cross-border e-commerce platforms and employs natural language processing techniques, specifically LDA topic modeling and BERT-based sentiment analysis, to explore consumer feedback on language services—including product translation, cultural adaptation, and customer service communication. The findings reveal that consumer reviews mainly center on four key themes: product quality, logistics service, price perception, and taste and flavor. Among these, language-service-related feedback primarily concerns translation accuracy, cultural appropriateness of expression, and customer service communication efficiency. Based on the sentiment analysis results, this study proposes optimization strategies for language services in cross-border agricultural e-commerce, including the development of standardized terminology databases, AI-assisted translation enhancement, and cross-cultural expression adaptation, thereby providing decision-making support for language services in agricultural product exports.