• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.117-126
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• 2013

Recently, as Social Network Services(SNS), such as Twitter, Facebook, are becoming more popular, much research has been doing on opinion mining. However, current related researches are mostly focused on sentiment classification or feature selection, but there were few studies about opinion document retrieval. In this paper, we propose a new retrieval method of short opinion documents. Proposed method utilizes previous sentiment classification methodology, and applies several features of documents for evaluating the quality of the opinion documents. For generating the retrieval model, we adopt Learning-to-rank technique and integrate sentiment classification model to Learning-to-rank. Experimental results show that proposed method can be applied successfully in opinion search.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.27-35
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• 2013

Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.222-226
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• 2010

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through these reviews. However, since online shopping malls do not provide ranking results, it is not easy for users to read all the relevant review documents effectively. Product reviews include subjective and emotional opinions. Thus, the review search is different from the general web search in terms of ranking strategy. In this paper, we propose an effective method of ranking the reviews that can reflect user's intention by using opinion mining techniques. The proposed method analyzes product reviews with query words, and sentimental polarity of subjective opinions. Through diverse experiments, we show that our proposed method outperforms conventional ones.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.399-401
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• 2016

제안된 시스템은 다양한 소셜 네트워크에서 사용자가 입력한 키워드를 기반으로 데이터를 수집하여 형태소 분석을 거쳐 사용자에게 통계정보 및 키워드에 대한 오피니언 마이닝 결과를 제공한다. SNS 상에 수많은 정보들이 저장되는데, 이를 이용하는 과정에서 단편적인 정보밖에 얻을 수 없는 비전문적인 사용자에게 유용한 데이터를 제공하기 위해 Opinion Mining 및 다양한 통계적 분석을 통해 키워드에 대한 시각화 정보를 출력한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1145-1146
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• 2013

인터넷과 스마트폰의 발달로 SNS서비스의 사용자와 데이터가 활발하게 증가하고 있다. 이로 인하여 SNS 데이터의 가치와 신뢰성이 점점 증가하고 있으며, 이러한 추세에 따라 여러 연구와 실험을 통하여 데이터를 분석하고 분석 결과를 제공하는 서비스가 증가하고 있다. 본 논문에서는 이러한 배경을 바탕으로 특정 키워드를 포함하고 있는 한글 트윗을 검색하여 해당 트윗에 대한 연관 키워드와 감정 키워드를 분석해서 출력해주는 시스템을 개발한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1184-1187
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• 2011

스마트폰 시장이 커지면서, 사람들이 하루에 업로드하고 다운로드하는 어플리케이션의 수 또한 급격히 증가하고 있다. 앱스토어와 안드로이드마켓에 등록된 어플리케이션의 종류는 어마어마하며, 사람들은 자신의 생활을 편리하게 해줄 어플리케이션 혹은 재미를 위한 어플리케이션을 다운로드하고자 한다. 하지만 현재 어플리케이션에 대한 평가는 점수로만 이루어져있기 때문에 어느 부분에서 뛰어난지, 어떤 부분의 기능이 떨어지는지는 사용자가 알 수 없고, 특정 기능을 중요시하는 사용자일 경우 별점이 높아도 해당기능이 만족스럽지 않으면 만족감의 정도는 대단히 떨어지게 된다. 그러면 다른 어플리케이션을 받아 같은 작업을 반복해야하는데, 이 경우가 반복될 경우 비용적인 문제뿐만 아니라 사용자에게 매우 번거로운 일이다. 따라서 본 논문에서는 기존 사용자들이 자신이 사용한 어플리케이션에 대해 작성한 후기를 오피니언 마이닝 기술을 적용시켜 각 키워드별, 즉 속성별로 평가하고 긍정/부정 여부를 데이터베이스에 저장하여, 해당 어플리케이션을 검색한 미래의 어플리케이션 사용자에게 시각적으로 정보를 알려주어 사용자의 수고를 덜어주고자 한다. 어플리케이션 다운로드가 매우 단순한 작업이지만, 다운로드 수가 많기 때문에 본 논문의 제안을 적용한다면 비용을 절감시켜 줄 뿐만 아니라 매우 효율적인 작업이 될 것이라 기대한다.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.559-568
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• 2014

Previous polarity classification using sentiment analysis utilizes a sentence rule by product reviews based rating points. It is difficult to be applied to blogs which have not rating of product reviews and is possible to fabricate product reviews by comment part-timers and managers who use web site so it is not easy to understand a product and store reviews which are reliability. Considering to these problems, if we analyze blogs which have personal and frank opinions and classify polarity, it is possible to understand rightly opinions for the product, store. This paper suggests that we extract high frequency vocabularies in blogs by several domains and choose topic words. Then we apply a technique of sentiment analysis and classify polarity about contents of blogs. To evaluate performances of sentiment analysis, we utilize the measurement index that use Precision, Recall, F-Score in an information retrieval field. In a result of evaluation, using suggested sentiment analysis is the better performances to classify polarity than previous techniques of using the sentence rule based product reviews.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.192-194
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• 2012

선거는 한 국가의 발전에 큰 영향을 미치는 중요한 행사이다. 국민들의 선거에 대한 관심은 해가 갈수록 증가하고 있고, 선거철마다 많은 여론이 형성되고 있다. 유권자들은 자신이 원하는 후보를 선정하기 위해 많은 후보자 정보를 살펴보아야한다. 올바른 판단을 위해서는 수많은 정치인과 정당에 대한 사전 분석이 필요할 것이다. 이는 시사나 정치에 대한 지속적인 관심이 요구되기 때문에 쉬운 일이 아니다. 그래서 후보자에 관한 기사나 공인된 온라인 토론에서의 정보를 검색하고 점수화하여, 투표자들이 후보를 결정하는데 도움을 줄 수 있는 방법을 제안한다.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.395-404
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• 2015

This study is exploring objective awareness of literary therapy by consideration of popular perception about literary therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service(SNS) about 'literary therapy'. Accordingly, the main way of research became content analysis of keyword linked to literary therapy by utilizing opinion mining method related to text mining. The study mainly grasped 'literary therapy' and analyzed 'bibliotherapy' comparatively. The period of study was from Oct. 10th to Nov. 10th, 2014(during 30 days), and SNS such as blog or twitter became the subject of search. Through the result of study analysis, the conclusion that the spread of literary therapeutic prospect, structural harmony of literary therapeutic field, and the solidity of perceptional axis about literary therapy are needed can be drawn. This study is worthwhile because it can investigate popular awareness about literary therapy and can suggest alternative for invigoration of literary therapy.