Secondhand seller reputation in online markets: A text analytics framework

With the rapid development of e-commerce, a new type of secondhand e-commerce website has appeared in recent years. Any user can have his or her own shop and list superfluous items for sale online without much supervision. These secondhand e-commerce platforms maximize the economic value of secondhand markets online, but buyers risk conducting unpleasant transactions with low-reputation sellers. The main contribution of our research is the design of a text analytics framework to assess secondhand sellers' reputation. In addition, we develop a new aspect-extraction method that combines the results of domain ontology and topic modeling to extract topical features from product descriptions. We conduct our experiments based on a real-word dataset crawled from XianYu. The experimental results reveal that our ontology-based topic model method outperforms a traditional topic model method. Furthermore, the proposed framework performs well in different item categories. The managerial implication of our research is that potential buyers can prejudge the reputation of secondhand sellers when making purchase decisions. The results can support a more effective development of online secondhand markets.