The Impact of Sentiment Scores Extracted from Product Descriptions on Customer Purchase Intention

Abstract

AbstractThis study investigates whether and how the textual content of product descriptions, especially the sentiment element, influences buyers’ purchase intentions. Using year-round digital transaction data from Mercari, a leading e-Commerce platform in Japan, we examine the interplay of hard and soft information signals exchanged between sellers and buyers. The study addresses two crucial questions: (1) Do the descriptions that sellers provide on product sales pages impact the buyer’s intent to purchase? and (2) In what way does the description influence the buyer’s purchase intention? Quantitative analysis is used to understand the relationship between product descriptions, sentiment elements, and purchase intentions. The results show that sentiment factors in product descriptions can serve as high-quality “signals” that can help buyers make informed purchasing decisions and reduce information asymmetry between buyers and sellers. This research contributes to understanding decision-making in online markets, particularly the role of soft information and sentiment analysis.