What Do People Write in Reviews for Sellers?—Investigation and Development of an Automatic Classification System—

Abstract

Herein, we describe a system that classifies each sentence in reviews for the sellers of an online shopping website based on aspects, such as shipping and packaging, and sentiment polarity (positive and negative). First, we investigated 487 sentences that were extracted from randomly selected 100 seller reviews for revealing the aspects mentioned in the reviews. This was done because the aspects in the seller reviews are not obvious. Consequently, we found that 14 aspects were described in the seller reviews. We annotate 14 aspects and their sentiment polarity for 5,277 sentences in 1,510 seller reviews that are newly and randomly chosen. Then, we train the classification models using the existing machine learning software. Through the system based on these classification models, users can understand the trend for any aspect in a time series and easily access reviews describing aspects which they are interested in.