• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.260-265
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• 2010

웹 2.0시대의 도래에 따라 많은 소비자들은 상품에 대한 다양한 의견을 표현할 수 있게 되었다. 이러한 의견들을 활용하여 상품평 요약 시스템 등이 개발되었다. 어휘의 의미 극성은 이러한 시스템에서 활용될 여지가 많은 요소이다. 영어의 경우 어휘의 의미 극성을 판단하는 연구가 많이 진행되어 어느 정도 결실을 맺었지만, 우리말의 경우 어휘의 의미 극성을 판단하는 연구는 아직 미흡하다. 본 논문에서는 우리말 어휘의 의미 극성을 PMI를 사용하여 판단한다. 또한 PMI를 우리말 어휘에 적용할 때 문제가 되는 이슈를 살펴보고 이에 대한 해결 방법들을 제시한다. 나아가 실제 상품 평에서 많이 쓰이는 형용사에 대하여, 제시한 의미 극성 판단 방법의 성능을 검증해 본다. 제시한 방법은 어휘의 의미 극성을 81%의 정확도로 판단해 주었다.

• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.125-137
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• 2011

Product reviews in online shopping sites can serve as a useful guideline to buying decisions of customers. However, due to the massive amount of such reviews, it is almost impossible for users to read all the product reviews. For this reason, e-commerce sites provide users with useful reviews or statistics of ratings on products that are manually chosen or calculated. Opinion mining or sentiment analysis is a study on automating above process that involves firstly analyzing users' reviews on a product to tell if a review contains positive or negative feedback, and secondly, providing a summarized report of users' opinions. Previous researches focus on either providing polarity of a user's opinion or summarizing user's opinion on a feature of a product that result in relatively low usage of information that a user review contains. Actual user reviews contains not only mere assessment of a product, but also dissatisfaction and flaws of a product that a user experiences. There are increasing needs for effective analysis on such criteria to help users on their decision-making process. This paper proposes a model that stores various types of user reviews in a data warehouse, and analyzes integrated reviews dynamically. Also, we analyze reviews of an online application shopping site with the proposed model.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.1-15
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• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.243-253
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• 2012

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1047-1050
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• 2015

Wearable device, along with IoT(Internet of Things), is considered the core of upcoming generation's convergence technology. Companies are intensely competing one another for prior occupation in the smartwatch market. Consumers that use smartwatch express their preferences by sharing their opinions through SNS(Social Networking Service). Through this study, emotions dictionary is built, which consists of attributes and emotional words related to smartwatch. Based on the emotions dictionary, SNS data has been categorized according to the attributes through opinion data model. Afterwards, overall polarity and attribute polarity of collected data are distinguished through natural language parsing, followed by an analysis of smartwatch reviews. This study will contribute to determination of which attributes of smartwatch to be improved, to arise consumer's interest for individual smartwatch.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.595-607
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• 2021

Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.