• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.108-111
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• 2015

아직까지도 도서 구매자의 대다수는 도서를 구매할 시 오프라인 서점을 이용하며, 외부 의견은 도서 구매 결정에 커다란 영향을 마치는 것으로 나타났다. 이에 따라 대표적 외부 의견인 도서의 리뷰를 가공 분석하여 제공하는 모바일 기반의 시스템의 필요성이 대두되었다. 하지만 현재 마켓에 등록된 애플리케이션의 대다수는 도서에 대한 사용자의 리뷰를 제공하지 않거나 분류 분석되지 않은 상태의 리뷰를 제공한다. 따라서 본 논문에서는 각 도서의 리뷰를 수집하여 리뷰의 긍정 부정적 감정 추이를 분석하고 그 결과를 리뷰 핵심어에 따라 분류된 도서 평가 기준 별로 제공하며 이를 통해 사용자의 도서 구매 결정과 여러 도서간의 도서 선택에 도움을 줄 수 있는 모바일 애플리케이션을 설계하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.729-732
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• 2018

모바일 게임 산업의 발달로 많은 사용자들이 게임을 이용하면서, 그들의 만족감을 사용리뷰를 통해 드러낸다. 실제로 각 리뷰의 범주가 모두 다르지만 현재 구글 플레이 앱스토어(Google Play App Store)의 게임 리뷰 범주는 3가지로 매우 제한적이다. 따라서 본 연구에서는 빠르고 정확한 고객의 요구를 필요로 하는 게임 소프트웨어의 특성을 고려하여 게임 리뷰를 입력했을 때, 게임의 운영 및 시스템에 맞도록 리뷰의 카테고리를 세분화하고 만족도를 분석하는 시스템을 개발한다. 제안 시스템은 인공신경망 모델인 CNN을 평점을 기반으로 훈련시켜 리뷰에 대한 만족도를 도출한다. 또한 Word2Vec을 이용해 단어들 간의 유사도를 구하고, 이를 활용한 단어 배열을 이용하여 가장 스코어가 높은 카테고리로 배정한다. 본 논문은 제안한 리뷰 만족도 및 카테고리 분류 시스템이 실제 효과적으로 리뷰를 보다 의미 있는 정보로써 제공할 수 있음을 보인다.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.209-217
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• 2016

As smartphone usage increases, the number of consumers who refer to review data of e-commercial products using web sites and SNS is also explosively multiplying. However, reading review data using traditional websites and SNS is time consuming. Also, it is impossible for consumers to read all the reviews. Therefore, a system that collects review data of products and conducts sentiment word analysis of the review is required to provide useful information. The majority of systems that provide such information inadequately reflect the properties of the product. In this study, we described a system that provides analysis and evaluation of e-commerce products through review sentiment words as reflected properties of the product. Furthermore, the system enables consumers to access processed information about reviews quickly and in visual format.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.39-46
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• 2021

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.19-27
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• 2023

In the process of collecting social reviews, a number of noise reviews irrelevant to a given search keyword can be included in the search results. To filter out such reviews, machine learning can be used. However, if the number of reviews is insufficient for a target place to be analyzed, filtering accuracy can be degraded due to the lack of training data. To resolve this issue, we propose a supervised learning method to improve accuracy of the noise review filtering for the places with insufficient reviews. In the proposed method, training is not performed by an individual place, but by a group including several places with similar characteristics. The classifier obtained through the training can be used for the noise review filtering of an arbitrary place belonging to the group, so the problem of insufficient training data can be resolved. To verify the proposed method, a noise review filtering model was implemented using LSTM and BERT, and filtering accuracy was checked through experiments using real data collected online. The experimental results show that the accuracy of the proposed method was 92.4% on the average, and it provided 87.5% accuracy when targeting places with less than 100 reviews.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.316-330
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• 2020

The study analyzes reviews of hardware products, customer service products, and products that take the form of a convergence of hardware and cloud services in ICT using text mining. We derive keywords of each review and find the differentiation of words that are used to derive topics. A cluster analysis is performed to categorize reviews into their respective clusters. Through this study, we observed which keywords are most often used for each product type and found topics that express the characteristics of products and services using topic modeling. We derived keywords such as "professional" and "technician" which are topics that suggest the excellence of the service provider in the review of service products. Further, we identified adjectives with positive connotations such as "favorite", "fine", "fun", "nice", "smart", "unlimited", and "useful" from Amazon Eco review, an integrated product and service. Using the cluster analysis, the entire review was clustered into three groups, and three product type reviews exclusively resulted in belonging to each different cluster. The study analyzed the differences whereby consumer needs are expressed differently in reviews depending on the type of product and suggested that it is necessary to differentiate product planning and marketing promotion according to the product type in practice.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.30-40
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• 2021

Ratings and reviews, despite their growing influence on restaurants' sales and reputation, entail a few limitations due to the burgeoning of reviews and inaccuracies in rating systems. This study explores the texts in reviews and ratings of a delivery application and discovers ways to elevate review credibility and usefulness. Through a text mining method, we concluded that the delivery application 'Yogiyo' has (1) a five-star oriented rating dispersion, (2) a strong positive correlation between rating factors (taste, quantity, and delivery) and (3) distinct part of speech and morpheme proportions depending on review polarity. We created a chicken-specialized negative word dictionary under four main topics and 20 sub-topic classifications after extracting a total of 367 negative words. We provide insights on how the research on delivery app reviews should progress, centered on fried chicken reviews.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.272-284
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• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.631-634
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• 2008

인터넷을 이용한 사람들의 사회 참여가 확대되면서 다양한 의견(Opinion)들이 급속도로 증가하고 있으며 이러한 의견을 분석하여 유용한 정보로 활용하기 위한 연구가 활발히 진행되고 있다. 그 중에서도 상품리뷰는 기업에서 연구, 개발, 마케팅의 주요 자료로 사용되고 있으며 사용자가 상품의 구매를 결정하는 중요한 요인 중 하나로 작용하고 있다. 본 논문에서는 한국어로 이루어진 상품 리뷰를 분석하여 의견 자질(Feature)을 추출하고 분류(Classification)하는 시스템을 설계하고 구현하였다. 한글 의견 자질 추출을 위하여 먼저 한글 상품 리뷰를 분석하여 의견 사전을 구축하였다. 의견 사전으로는 의견 자질과 의견 어휘, 독립의견어휘, 의견 숙어, 부정어 등의 각기 다른 세부 사전을 구축하여 리뷰 분석 시 단계적으로 적용하여 정확도를 높일 수 있도록 설계하였다. 이렇게 구현된 시스템을 평가하기 위하여 각기 다른 3개의 도메인에서 실제 한국어 리뷰를 수집하여 실험을 수행하였으며 자질 추출에서는 평균 78.86% 정확률, 61.41% 재현율을, 극성 분류에서는 평균 69.46% 정확률, 42.26% 재현율을 나타냈다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1234-1241
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• 2008

Cost effectiveness is greatly related with the degree of reducing the testing cost by the technical reviews. In this paper, we present a new metric My for evaluating the cost effectiveness of technical reviews during software development. First, we estimate and compare My with conventional measure using data collected during practical software development procedure, then we show the validity and usefulness of the proposed measure My. Also by formulating the relationship between the data collected during the reviews and the test, we present a method to estimate the value of the metric My using only the data collected during review phase.