• Title, Summary, Keyword: 오피니언 마이닝

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Fuzzy Domain Ontology-based Opinion Mining for Transportation Network Monitoring and City Features Map (교통망 관찰과 도시 특징지도를 위한 퍼지영역 온톨로지 기반 오피니언 마이닝)

  • Ali, Farman;Kwak, Daehan;Islam, SM Riazul;Kim, Kye Hyun;Kwak, Kyung Sup
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.1
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    • pp.109-118
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    • 2016
  • Traffic congestions are rapidly increasing in urban areas. In order to reduce these problems, it needs real-time data and intelligent techniques to quickly identify traffic activities with useful information. This paper proposes a Fuzzy Domain Ontology(FDO)-based opinion mining system to monitor the transportation network in real-time as well to make a city polarity map for travelers. The proposed system retrieves tweets and reviews related to transportation activities and a city. The feature opinions are extracted from these tweets and reviews and then used FDO to identify transportation and city features polarity. This FDO and intelligent prototype are developed using $Prot{\acute{e}}g{\acute{e}}$ OWL (Web Ontology Language) and JAVA, respectively. The experimental result shows satisfactory improvement in tweets and review's analyzing and opinion mining.

Friend Recommendation System Using Opinion Mining (오피니언 마이닝을 이용한 친구 추천 시스템)

  • Hwang, Su-Jin;Yoon, Jae-Yeol;Kim, Iee-Joon;Kim, Ung-Mo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.1188-1190
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    • 2011
  • 오피니언 마이닝은 웹에 있는 문서를 분석하여 작성자의 의견을 요약된 형태로 보여주는 기술이다. 오피니언 마이닝을 이용해 문서 작성자의 주관적 의견을 알 수 있고 이를 통해 작성자의 성향이나 관심사와 같은 정보를 얻을 수 있다. 많은 네티즌들은 소셜 네트워크 서비스를 통해 자신의 의견이 담긴 글을 타인과 공유 하며 네트워크상의 인맥을 넓혀 나간다. 오피니언 마이닝을 통해 개인이 작성한 글들을 분석하여 관심사를 파악하고 비슷한 관심사를 가진 친구를 추천하는 친구 추천 시스템을 제안한다.

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Opinion Mining of Product Reviews using Association Rules (연관 규칙을 사용한 상품평 오피니언 마이닝)

  • Kim, Won-Young;Ryu, Joon-Suk;Kim, Ung-Mo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.747-748
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    • 2009
  • 사용자가 웹 상에 작성한 상품평은 다양한 정보를 포함하고 있는 데이터이다. 대부분의 사람들이 상품을 구입하기 전에 상품평을 통해서 상품에 대한 많은 정보를 얻는다. 이에 따라 대량의 상품평 데이터로부터 유용한 정보를 추출하여 요약하는 오피니언 마이닝에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 사용자가 많은 상품평들을 모두 읽어보지 않고 상품에 대한 오피니언과 장점과 단점을 쉽게 알 수 있도록 연관 규칙 마이닝을 적용하는 오피니언 마이닝 방법을 제안한다.

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A Study on the Effect of Using Sentiment Lexicon in Opinion Classification (오피니언 분류의 감성사전 활용효과에 대한 연구)

  • Kim, Seungwoo;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.133-148
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    • 2014
  • Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

Expansion of Opinion Mining based on Entity Association Network Model (개체연관망 모델에 의한 오피니언마이닝의 확장)

  • Kim, Keun-Hyung
    • The KIPS Transactions:PartD
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    • v.18D no.4
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    • pp.237-244
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    • 2011
  • Opinion Mining summarizes with classifying sensitive opinions of customers in huge online customer reviews for the attributes of products or services by positive and negative opinions. Because the customers represent their interests through subjective opinions as well as objective facts, the existing opinion mining techniques, which can analyze just the sensitive opinions, need to be expanded.. In this paper, We propose the novel entity association network model which expands the existing opinion mining techniques. The entity association model can not only represent positive and negative degree of the sensitive opinions, but also can represent the degree of the associations and relative importances between entities. We designed and implemented the customer reviews analysis system based on the entity association network model. We recognized that the system can represent more abundant information than the existing opinion mining techniques.

Performance Analysis of Opinion Mining using Word2vec (Word2vec을 이용한 오피니언 마이닝 성과분석 연구)

  • Eo, Kyun Sun;Lee, Kun Chang
    • Proceedings of the Korea Contents Association Conference
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    • pp.7-8
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    • 2018
  • This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

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An Opinion Mining System for A Figurative Representation of Disabilities (장애인의 비유적 표현을 위한 오피니언 마이닝 시스템)

  • Kim, Chgan Gi;Seo, Jeong Min
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.95-96
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    • 2015
  • 사회복지 영역의 확대로 복지서비스 수혜자들의 사례관리가 매우 중요한 영역으로 자리매김하고 있다. 이는 사례관리를 이용하여 새로운 서비스를 발굴하고, 실행결과를 평가하여 중요한 패턴을 추출 후 다른 유사 대상자들에게 적용하는 것이 실패를 줄이는 방법이기 때문이다. 그러나 현재 대부분의 사례관리시스템은 서비스를 입력하여 저장/관리하는 측면만을 제공하여 체계적인 분석이 안되고 있다. 이에 본 논문에서는 사례자들의 상담 및 서비스 결과에 관한 오피니언을 분석하여 마음속에 내포하고 있는 사례(비유적 표현)에 관한 실제적인 평가와 오피니언을 추출하는 시스템을 제안한다. 제안하는 시스템을 실험하기 위해 자기의 오피니언을 외부로 노출하기 꺼려하는 장애인을 대상으로 한 상담 사례를 이용하여 실험하였다.

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Comparison and Analysis of Domestic and Foreign Sports Brands Using Text Mining and Opinion Mining Analysis (텍스트 마이닝과 오피니언 마이닝 분석을 활용한 국내외 스포츠용품 브랜드 비교·분석 연구)

  • Kim, Jae-Hwan;Lee, Jae-Moon
    • The Journal of the Korea Contents Association
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    • v.18 no.6
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    • pp.217-234
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    • 2018
  • In this study, big data analysis was conducted for domestic and international sports goods brands. Text Mining, TF-IDF, Opinion Mining, interestity graph were conducted through the social matrix program Textom and the fashion data analysis platform MISP. In order to examine the recent recognition of sports brands, the period of study is limited to 1 year from January 1, 2017 to December 31, 2017. As a result of analysis, first, we could confirm the products representing each brand. Second, I could confirm the marketing that represents each brand. Third, the common words extracted from each brand were identified. Fourth, the emotions of positive and negative of each brand were confirmed.

Efficient Retrieval of Short Opinion Documents Using Learning to Rank (기계학습을 이용한 단문 오피니언 문서의 효율적 검색 기법)

  • Chang, Jae-Young
    • 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.

Spam Filtering using Opinion Mining (오피니언 마이닝을 이용한 스팸 필터링)

  • Oh, Jin-Soo;Ryu, Joon-Suk;Kim, Ung-Mo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.745-746
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    • 2009
  • 오늘날 사람들의 의견을 제시하는 공간은 폐쇄적인 인쇄물이나 수동적인 답변 수준을 벗어나 무한의 공간을 가지는 웹에서 이루어지고 있다. 불특정 다수를 대상으로 하며 정형화된 틀을 없는, 더욱 유용한 의견을 많이 얻을 수 있는 특징을 가졌기 때문에, 이를 위해 오피니언 마이닝에 대한 연구가 활발히 진행되고 있다. 기본적으로 오피니언 마이닝은 해당 분야에 대한 정확한 정보를 찾는 것을 목적으로 하지만, 그러한 정보를 제외한 나머지 부분에 대해서도 충분히 유용하게 사용할 수 있다. 본 논문에서는 그 나머지 부분을 이용하여 무분별하게 등록되고 있는 스팸성 댓글을 효과적으로 필터링 할 수 있는 방법을 제안한다.

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