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After you place an order, Amazon shares some of your account information with the merchant to process your order. This includes your name, email, delivery address, phone number, and the last four digits of your payment method used during checkout.
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2.1. Natural language processing technology
Opinion data is extracted from text data that mentions the user's opinion about the object itself or some features of the object in a sentence, such as "This software updates really fast." In the case of Korean, this method uses "morphology analysis" and "phrase analysis" as a pre-processing step. Opinion text extracts information, such as the opinion expression, object, and modifier, after proper natural language processing, such as morpheme analysis and parsing, and then determines whether the meaning of a specific word is positive/negative, and strengthens or weakens the meaning to obtain a final polarity value. OLSAP sees the polarity information of the opinion data as an element corresponding to a "measurement", such as sales volume, uses polarity information in the opinion data, and obtains complex information, such as users' overall evaluation of products, regional evaluations, and changes in opinions over time (Myung et al., 2008).
2.5. Quantitative analysis of blogs using a random forest
The Support Vector Machine (SVM) is a supervised learning model that is mainly used for classification and regression analysis. The SVM uses support vectors to define decision boundaries and to classify unclassified points against those decision boundaries.
The study was conducted in the following figure of a flowchart.
There are two major limitations of this study. First, BERT, which was judged to be the most suitable as an aspect of natural language processing deep learning techniques, was not able to use the model because it was a very large model with 12 GB of GPU memory recommended for learning. The second limitation concerns the data set. The crawled data was provided by Naver Shopping Mall, which seems to have already been classified once, but as a result of asking Naver whether it was screened, details of backend filtering and classification algorithms could not be identified due to in-house confidentiality. In addition, we tried to increase the accuracy by analyzing more data, but there were limitations in analyzing more data because this analysis already required significant memory resources.
Jung S., Kim K., Im E., Kim D. (2020). A study on the impact of consumer review on mobile commerce performance. Global Bus. Adminis. Rev. 17, 219–314. 10.38115/asgba.2020.17.6.219 [CrossRef] [Google Scholar]
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