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.
Demand forecasting is the activity of predicting the future using historical data and establishing a model that can grasp trends. Demand forecasting is widely used in a variety of business areas, including production and inventory planning as well as process management. The goal of a company that publishes and sells books is to accurately predict sales volume, thereby increasing book sales, generating more revenue, and reducing losses from inventory management. Using data analysis to predict accurate publishing demand and establishing countermeasures against factors that may cause returns can reduce the amount of losses incurred due to inventory control and returns. The purpose of this study is to identify the factors affecting the sale and return of specific books and to create a model to forecast sales demand. For this purpose, we used the sales data of the books sold for 5 years (2012 ~ 2016) by A publishing company. In addition, we collected the data related to books in the Internet portal system and SNS site. We hypothesized the factors that affect the sale and return of books and collected the variables needed for hypothesis testing from web pages and SNS sites. As a result of this study, it was possible to identify the factors affecting the return and sales of a specific book, and it was possible to establish a sales order prediction model. Because the available data is limited in the study, the scope of this study was limited to forecasting the sales demand of some books. If we apply the proposed analytical procedure and method directly from the company, we can expect better prediction results. It is also expected to be applicable to various business processes of book publishing or sales companies.
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