Word Frequency Analysis and Classification of Keywords for Agricultural Product E-commerce in New Media Videos Using Clustering Algorithm

Abstract

With the rapid expansion of e-commerce platforms and the growing influence of new media, agricultural product marketing has undergone a significant transformation. In this context, understanding the dynamics of keyword usage in new media videos becomes crucial for effective marketing strategies. This paper presents a comprehensive study on word frequency analysis and classification of keywords for agricultural product e-commerce in new media videos, leveraging clustering algorithms. The study involves the collection and analysis of a large corpus of agricultural product-related videos from various new media platforms. Through advanced natural language processing techniques, including word frequency analysis, keyword extraction, and clustering algorithms, significant insights into the prevalent themes and trends in agricultural product marketing are revealed. The clustering algorithm applied helps categorize keywords based on their semantic similarities, enabling the identification of clusters representing distinct marketing strategies, product features, or consumer preferences. This classification facilitates targeted content creation, SEO optimization, and personalized marketing campaigns tailored to specific audience segments.