Automated Product Review Collection and Opinion Analysis Methods for Efficient Business Analysis

Abstract

In this paper, we propose a business analysis method that automatically collects review information related to a specific product by using a web crawler, analyzes the customer's emotional response to the product, and supports marketing activities. In order to process data collected from web pages and social networks into information that can be used for marketing, several levels of data processing and text mining techniques are needed. Although various studies have been carried out for this purpose, the data collected through lots of effort and cost contain more extensive information than needed for marketing. So the usefulness of the information obtained through data processing and analysis is not really good. In this paper, we propose a system that automatically receives data from interested sites, interested fields, interested keywords, and target period information from a user and crawls the data. In addition, the reviews that can be used only in marketing can be selected to judge whether or not they are positive, thus it improves the accuracy of the analysis. For the experiment, we collected the reviews of the books sold by specific publishers over the past three years and conducted reputation analysis. The proposed classifier distinguishes through supervisor whether the data collected is a proper review or not. The accuracy of the review classifier is 98.7%. The reputation analyzer judges whether the review is positive or negative with the 86.1% accuracy. The results of this study can be used directly in various industries. And we plan to develop the reputation analyzer, improving the accuracy and extracting the reputation factors affecting customers.