This study examined consumer perceptions and market trends of Korean food products sold on Amazon, focusing on keyword and review analysis. As Korean cuisine gains global attention, particularly in the U.S., it is essential to understand how international consumers perceive these products. Data were collected from Amazon, encompassing product details and customer reviews of Korean food categories. Frequently mentioned keywords in the reviews were identified, and customer sentiments were explored using Python-based data analysis. The results suggest that taste, especially spiciness and sweetness, is a key determinant of consumer satisfaction. Although Korean food was generally well-received, packaging and delivery issues were common complaints. These findings provide strategic insights for Korean food manufacturers aiming to expand globally, emphasizing the need for tailored product development and enhanced logistics.

온라인 크라우드소싱 플랫폼인 Amazon Mechanical Turk(MTurk)은 뛰어난 과제 수행 기록을 가진 참가자들에게 마스터 등급을 부여한다. 그러나 MTurk의 마스터 참가자와 일반 참가자를 비교한 선행 연구들은 두 집단이 실제로 수행의 차이를 보이는가에 대해 일관되지 않은 결과를 보고했다. 또한 선행 연구들은 대부분 설문 조사 방식을 사용 했으며 MTurk의 마스터와 일반 참가자의 인지 과제 수행 능력을 비교한 연구는 부족한 상황이다. 본 연구는 시각 기억 재인 과제를 사용하여 MTurk 마스터 및 일반 참가자와 오프라인에서 모집한 대학생 참가자 집단의 수행을 비교했다. 연구 결과, MTurk 마스터 참가자와 오프라인 참가자는 동일한 수준의 기억 수행을 보였다. 그러나 MTurk 일반 참가자의 기억 과제 수행은 마스터와 오프라인 참가자 집단의 결과와 차이를 보였다. 각 집단에서 기억 과제 정확률이 낮은 참가자를 제외한 후에도 동일한 결과가 나타났다. 이러한 결과는 온라인에서 참가자 집단을 적절히 선발하면 기존의 오프라인 실험 결과를 잘 재현할 수 있음을 보여준다. 동시에 본 연구의 결과는 온라인 크라우드소 싱 플랫폼의 참가자 집단이 균일하지 않으며, 집단 선정 방식에 따라 연구의 결과가 다르게 나타날 수 있음을 시사한다.

Consumers' online reviews have become more powerful in the Internet market. Consumers share reviews, post comments and constantly evaluate products online. In previous studies, the analysis of online reviews mainly focused on purchasing products based on consumers' own use experience, but in innovative products, it was difficult to find an analysis of product acceptor's response to product user reviews. In particular, there is no online review study of VR covered in this study. This study not only quantitatively analyzed online reviews of consumers who purchased VR products on Amazon, an online distribution site, but also qualitatively analyzed them through crawling. This study used Amazon's VR product user review, where purchases were confirmed, to select algorithms that are more likely to be matched by predicting a helpful review and presenting a predictive model. In addition, the online review extracted deep text associated with Helpful and conducted topical modeling. As a result, topics related to 1) experience in use, 2) post-product evaluation, 3) product composition and peripherals, 4) immersion, and 5) comfort were highly acceptable to potential inmates. To enhance the acceptability of innovative products through online reviews, it is not just highlighting the product advantages of VR, but also suggests that the link between smartphones and applications can bring in more potential users. Also, interworking with other peripheral devices (speakers or screens) can be predicted as a way to increase the acceptability of VR products. From a marketing perspective, this study has found targeted topics that help consumers in pioneering the VR market, which will help potential customers create the services they want.