• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.21-29
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• 2010

Opinion retrieval which searches the opinions expressed in documents by users cannot outperform significantly yet traditional topical retrieval which searches the facts. Therefore, the focus of this paper is to identify the statistical characteristics which can be applied to opinion retrieval by comparing and analyzing the term statistics of opinion and non-opinion documents in the blog domain. The TREC Blogs06 collection and 150 TREC topics are used in the experiments. The difference between term probability distributions in opinion documents is measured by JS divergence, and the difference according to the topic types and topic domains is also investigated. Moreover, the term probabilities of opinion terms are analyzed comparatively. The main findings of this study include the following: it is necessary to consider the topic-specific characteristics for the opinion detection; it is effective to extract positive and negative opinion terms according to the topics; the topic types are complementary to the topic domains; and special attention has to be given to the usage of the positive opinion terms.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.321-344
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• 2015

This paper presents a fusion approach to sentiment detection that combines multiple sources of evidence to retrieve blogs that contain opinions on a specific topic. Our approach to finding opinionated blogs on topic consists of first applying traditional information retrieval methods to retrieve blogs on a given topic and then boosting the ranks of opinionated blogs based on the opinion scores computed by multiple sentiment detection methods. Our sentiment detection strategy, whose central idea is to rely on a variety of complementary evidences rather than trying to optimize the utilization of a single source of evidence, includes High Frequency module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinionated documents), Low Frequency module, which makes use of uncommon/rare terms (e.g., "sooo good") that express strong sentiments, IU Module, which leverages n-grams with IU (I and you) anchor terms (e.g., I believe, You will love), Wilson's lexicon module, which uses a collection-independent opinion lexicon constructed from Wilson's subjectivity terms, and Opinion Acronym module, which utilizes a small set of opinion acronyms (e.g., imho). The results of our study show that combining multiple sources of opinion evidence is an effective method for improving opinion detection performance.

• 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 Society of Clothing and Textiles
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• v.21 no.1
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• pp.67-81
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• 1997

The main purpose of this study was to compare the characteristics of male innovators and of male opinion leaders across product categories in terms of personality, attitudes, social participation, media usage patterns and demographic aspects. Six product categories such as clothing, cosmetics, small electronic appliances, medium-large electronic appliances, interior supplies and sports-leisure goods was used. A valid and reliable self-report scale was used to measure innovativeness and opinion leadership for 423 male adults living in social area Analyses showed that venturesomeness is the most common characteristics between innovators and opinion leaders across product categories. Innovators showed higher tendency of narcissism while opinion leadership showed higher cosmopolitainsm. Common charateristics of innovators and of opininion leaders of both clothing and cosmetics are cosmopolitanism, narcissism, exhibitionism, venturesomeness. The degrees of social participation and media usage patterns were different according to product categories for both innovators and opinion leaders. The implications of these findings for diffusion theory and merchandising were discussed.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.167-176
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• 2013

User-created text data are increasing rapidly caused by development of social media. In opinion mining, User's opinions are extracted by analyzing user's text. A primary goal of sentiment analysis as a branch of opinion mining is to extract user's opinions from a text that is required to build a list of emotion terms. In this paper, we built a list of emotion terms to analyse a sentiment of social media using Facebook as a representative social media. We collected data from Facebook and selected a emotion terms, and measured the dimensions of valence and activation through a survey. As a result, we built a list of 267 emotion terms including the dimension of valence and activation.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.121-133
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• 2018

This study begins with focus on the public opinion and political views on embracing Yemeni refugees in Jeju. The public opinion is demanding amendment or even abolition of the Refugee Act. The argument opposing Yemeni refugees is persuasive enough in terms of public order and safety, cultural conflicts and economic interests, but it is going against the universal and authentic values that must be pursued by a forward-looking society. It is natural that the power exerting political discretion keeps an eye on public opinion. However, no matter how many people express it, unjustified public opinion must not be a foundation for political decision. This study will examine the shadows of public opinion and related concepts for those reasons, and review Augustine's concept of 'love' as a value to refer to in making a communal decision. Conflicts lead to insecurity and rift. Without sensus communis (common sense), the rift will not be healed. This study raises the need to resolve such conflicting state and seeks insight from Augustine.

• Journal of Broadcast Engineering
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• v.12 no.2
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• pp.177-184
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• 2007

In this paper, we compared two subjective assessment methods DSCQS(Double Stimulus Continuous Quality Scale method) and ACR(Absolute Category Rating). These methods are widely used in order to evaluate video quality for multimedia application. We performed subjective quality tests using DSCQS and ACR methods. The subjective scores obtained by the DSCQS and ACR methods show that these methods are highly correlated in terms of MOS(Mean Opinion Score) and have slightly lower correlation in terms of DMOS(Difference Mean Opinion Score). The results indicate that ACR method is an effective subjective quality assessment method, which shows compatible performance with DSCQS method and can evaluate a larger number of video sequences.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.97-104
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• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• The Research Journal of the Costume Culture
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• v.9 no.1
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• pp.86-99
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• 2001

The purpose of this study was to identify and profile store browsers in terms of their browsing motives, fashion behavioral characteristics, buying behavior and preferred store attributes. The data were collected through questionnaire from 302 female college students by convenient sampling method. Statistical analysis of factor analysis, x²-test, and t-test were performed in analyzing the data. The browsing motives of browsers were to obtains fashion information, sensory stimulation and diversion from routine life. They showed the high level of fashion involvement, shopping confidence, shopping innovativeness, shopping opinion leadership as well s fashion opinion leadership. Browsers tended to be impulse buyers and spent more money on clothing than non-browsers. The attributes that influence their store choice were the variety of products and brands, information availability ,and pleasant store atmosphere.

• English Language & Literature Teaching
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• v.15 no.2
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• pp.121-150
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• 2009

This paper deals with contrastive analysis and interlanguage with respect to adjective ordering. It aimed to investigate how similar and different the orders of descriptive adjectives are in English and Korean, and how Korean EFL learners perceive the sequences of English descriptive adjectives. Data were collected from native English speakers and native Korean speakers and Korean EFL learners. The contrastive analysis showed that the order of English adjectives was size, opinion, condition, age, color, shape, material, and origin, whereas the Korean order was condition, age, opinion, color, size, shape, material, and origin. The relative order of the interlanguage was shown to be age, size, opinion, shape, condition, color, origin, and material, with the exceptions of the order of condition preceding age and that of size being the same position as condition. The interlanguage data manifested different aspects of ordering when compared with English and Korean: Some adjective combinations were similar to both English and Korean; Some were different from English or Korean; Some were different from both English and Korean. These ordering patterns are discussed in terms of such principles as the nouniness principle, the subjectivity/objectivity principle, the iconic principle, etc. On the basis of these results, some helpful suggestions are made.