The impact of sentiment scores extracted from product descriptions on customer purchase intention

抄録

A key challenge for e-commerce platforms is how to build trust between buyers and sellers and help buyers make better purchasing decisions. In this regard, researchers are interested in addressing the information asymmetry between buyers and sellers. In this study, we focus on featured products that are often sold at a higher price than the original price and examine whether signals hidden in the seller's presentation of such products can mitigate this information asymmetry. To do so, we compute a sentiment score for each product presentation text based on word frequency through text analysis. Finally, we drop this sentiment score into a logistic regression model to see if these variables can significantly influence buyers' purchase intentions as signals. In conclusion, we find that the calculated sentiment scores can have a significant impact on customers' purchase intentions and can be regarded as a new signal to reduce information asymmetry between buyers and sellers.