Along with the advancement in technology, todays community begins to abandon conventional shopping methods where buyers must come to the seller's shop. Nowadays community mostly doing online shopping because the process is considered more convenience. Because of this, there are more and more online marketplace users. Much more data can be retrieved with the increasing number of online marketplace users. Because of the large amount of data the process for extracting the data so that it can be seen and utilized becomes possible. The purpose of this journal is to show data and extraction method from an online marketplace system so that the results can be visualized and users can analyze the data. The data extraction method that will be used is the web crawling method and web scraping where after the data is successfully extracted and cleaned it will be visualized with the power BI application. The experiments show that the method is useful to conduct analysis.
With the rapid development of new technologies in smartphones, understanding market trends has become an increasingly difficult task. In these circumstances, online product reviews that can reflect consumer sentiment about the product has been a concern for now. Online review analysis can help sellers understand consumer interests and desires prior to launching a new product. The author wants to contribute to seeing the state of the smartphone market by creating a method to find important aspects of a smartphone. The data source comes from the Amazon e-commerce web with 4 predefined smartphone brands. In this study, the authors used topic modeling with the LDA algorithm and sentiment analysis with VADER to find aspects of a smartphone and its sentiment classification. From the 15 scenarios made in this reserach, it is found 3 aspects that always appear, namely the screen, camera, and battery aspects, so it is concluded that these 3 aspects are the most important of a smartphone based on textual reviews. Keywords— Topic Modeling; Sentiment Analysis; LDA; ecommerce
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