• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.882-884
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• 2019

오늘날 정보화 시대에서는 온라인 쇼핑의 상품리뷰 등 대용량의 텍스트 문서가 존재하며 제품에 대한 정서적인 의견뿐만 아니라 제품 선호도 및 상품 비교와 같은 유용한 정보를 제공한다. 본 논문에서는 사용자가 작성한 상품 리뷰로부터 제품의 특성을 비교하는 비교의견을 추출하기 위해 적용한 다양한 텍스트 마이닝 기법의 비교 결과를 제시한다.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.58-73
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• 2022

The purpose of this study is to construct a predictable estimation model that reflects the relationship between the variables of mobile app performance and to verify how app reviews affect app performance. In study 1 and 2, the relationship between app performance indicators was derived using correlation analysis and random forest regression estimation of machine learning, and app performance estimation modeling was performed. In study 3, sentiment scores for app reviews were by using sentiment analysis of text mining, and it was found that app review sentiment scores have an effect one lag ahead of the number of daily installations of apps when using multivariate time series analysis. By analyzing the dissatisfaction and needs raised by app performance indicators and reviews of apps, companies can improve their apps in a timely manner and derive the timing and direction of marketing promotions.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.338-343
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• 2016

Today, opinion reviews on the Web are often used as a means of information exchange. As the importance of opinion reviews continues to grow, the number of issues for opinion spam also increases. Even though many research studies on detecting spam reviews have been conducted, some limitations of gold-standard datasets hinder research. Therefore, we introduce a new dataset called "Paraphrased Opinion Spam (POS)" that contains a new type of review spam that imitates truthful reviews. We have noticed that spammers refer to existing truthful reviews to fabricate spam reviews. To create such a seemingly truthful review spam dataset, we asked task participants to paraphrase truthful reviews to create a new deceptive review. The experiment results show that classifying our POS dataset is more difficult than classifying the existing spam datasets since the reviews in our dataset more linguistically look like truthful reviews. Also, training volume has been found to be an important factor for classification model performance.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.535-540
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• 2021

Multi-document refers to a document consisting of various topics, not a single topic, and a typical example is online reviews. There have been several attempts to summarize online reviews because of their vast amounts of information. However, collective summarization of reviews through existing summary models creates a problem of losing the various topics that make up the reviews. Therefore, in this paper, we present method to summarize the review with minimal loss of the topic. The proposed method classify reviews through processes such as preprocessing, importance evaluation, embedding substitution using BERT, and embedding clustering. Furthermore, the classified sentences generate the final summary using the trained Transformer summary model. The performance evaluation of the proposed model was compared by evaluating the existing summary model, seq2seq model, and the cosine similarity with the ROUGE score, and performed a high performance summary compared to the existing summary model.

• KDI북한경제리뷰
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• v.23 no.3
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• pp.49-70
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• 2021

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• Life and Agrochemicals
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• s.247
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• pp.28-31
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• 2009

1990년 중반이후 저항성 잡초에 대한 방제체계가 확립되지 못함에 따라 제초제의 사용량이 더욱 늘었다. 그러다가 2000년 이후부터 단위 면적당 투여량이 적은 제초제가 늘면서 유효성분 함량이 낮은 약제끼리 합제로 개발되고 있어 성분이 낮은 제초제가 많이 늘어나고 있다.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.147-162
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• 2019

This study investigates the relationship between the online WOMs(such as volume of blogs, articles, reviews, searches) and the number of audience of successful film.The results are as follow: Frist, using a curve-estimation method, the results show that the longitudinal trends of the online WOMs can be best described by a cubic indicating. Second, using panel analysis in model(t) the volume of blogs, reviews, and searches is positively associated with the number of audience. All of the variables' coefficient are significant. However the volume of articles is negatively related to the number of audience with a significant coefficient.

• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.543-572
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• 2012

This paper compared historical energy use from 2000 between Japan and Korea and reviewed literature of mid-and long-term low carbon energy scenarios and plans in both countries released since 2000. In terms of energy use pattern, there are similarities between Korea and Japan; high dependence on energy imports, high proportion of manufacturing industry among OECD countries, closed electricity system disconnected with foreign countries, and high proportion of nuclear power generation with low proportion of renewable electricity despite of high potential of renewable energy. Differences are as follows; decreasing trend in Japan and increasing trend in Korea in terms of energy demand and supply, difficulty of exchanging electricity between regions in Japan unlike Korea, and prospect of nuclear power, that is, curtailing in Japan while expanding in Korea according to governmental plan. Energy Basic Plan in both countries established before nuclear accident in Fukushima required expanding about two times of nuclear energy by 2030, while civil society's energy scenarios suggested reducing energy demand, phasing-out nuclear power, and expanding renewable energy. This paper will serve as a base for future studies about long-term energy scenarios and plan in Japan and Korea.