ABSTRACT
Due to the impact of COVID-19, most people have to quit their jobs. As a result of the epidemic, many people are turning to open online stores on e-commerce platforms, and people new to this market are inexperienced in competing with other stores. One of the critical points that can help them is to present their stories through merchandising. The presentation of a story through sales can be like telling a story about their store or the product. However, writing a good story is not easy. So, this research aims to help the sellers by constructing linguistic resources that sellers can use for writing a story about their products. In this paper, 3378 token product description texts were collected and ranked by the Text Ranking method to determine the frequency of frequently-used words. The 1,853 words from the Text Ranking were applied for text modeling using Latent Dirichlet Allocation (LDA). The result shows that these words can be categorized into seven food and beveragerelated topics. Moreover, sellers can use these words for writing product descriptions. © 2022 IEEE.