오피니언 분류의 감성사전 활용효과에 대한 연구

김승우(Seungwoo Kim),김남규(Namgyu Kim) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.1

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.

온라인 제품리뷰의 분석을 위한 오피니언 마이닝 기법의 적용

진영택 한국지식정보기술학회 2021 한국지식정보기술학회 논문지 Vol.16 No.1

Understanding product characteristics and opinions from product reviews provided in news, reviews, and discussions on the web site help individuals make decisions about purchasing products. Companies need to improve product quality and design efficient marketing strategies. For this, opinion mining is required to find and analyze people's opinions from unstructured text reviews. Opinion mining aims to find people's opinions and feelings about certain products and services. It includes a variety of methods and techniques ranging from finding simple opinions on products or services to comparative opinions for products. In particular, comparative opinions provide practical information compared to simple opinions and emotions because people with experiences related to the product or service to be compared mainly provide specific information on the strengths, weaknesses, and differences of the product or service. However, it is not easy to classify comparison opinions from user reviews, and there are many techniques for this. In this study, we present the comparative results of applying various machine learning techniques to specific product reviews for comparative opinion mining. Through this, it is intended to help understand various techniques for classifying comparative opinions and extracting useful information such as the preference of product attributes.

Mining Opinion Word from Customer Review

Jiang Tengjiao,Zhong Minjuan,Liao Shumei,Luo Siwen 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.2

Online customer review is considered as a significant informative resource which is useful for both potential customer and product manufacturers. As a result, it is one of the most challenging tasks to mine customer reviews automatically and to provide users with opinion summary. Product features and opinion word play the most important roles in the customers’ opinions mining. In this paper, we dedicate our work to opinion word mining. We proposed an approach for opinion word identification based on the association rule mining algorithm. The method makes full use of co-occurrence syntactic characteristic between product features and opinion word. Firstly, the product feature is identified by two-stage filtering scheme, and secondly the opinion word is extracted through association rule mining. The final experiment results show that the proposed method could not only obtain the product features related to domain characteristics, but identify the opinion word effectively. Meanwhile, our approach possesses much higher precision and recall than Hu’s work.

Visualizing the Results of Opinion Mining from Social Media Contents

Yoosin Kim(김유신),Do Young Kwon(권도영),Seung Ryul Jeong(정승렬) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.4

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean “Ramen” business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the resu

SVM을 이용한 배달 애플리케이션 음식점 리뷰의 자동 별점 분류

고동환,김홍준 大田大學校 産業技術硏究所 2017 산업기술연구소 論文集 Vol.28 No.2

Opinion mining is widely used for various field to analyze opinions, attitudes, and emotions. However, studies concerned with opinion mining for restaurant reviews of delivery applications are few. We conducted opinion mining using a trained machine learning model in another field or already built sentiment lexicon. But it can degrade accuracy. Therefore, in this paper, trains support vector machine model with restaurant reviews and then conducts Opinion Mining by classifying it based on the rating of reviews. To apply the SVM algorithm to reviews consisting of text, convert it to numerical data using natural language processing and text mining. Consider each rating as a class, classify reviews. And then, compare error between original rating and classified rating. As experimental result, approximately 63% of reviews were correctly classified, and 24% of reviews were classified with error size of 1. In addition, several terms that have close relation with the delivery applications were found. 다양한 분야에서 오피니언 마이닝을 활용하지만 배달 모바일 애플리케이션의 음식점 리뷰에 대한 사례는 거 의 없는 상황이다. 다른 분야에서 학습된 기계 학습 모델 또는 이미 구축된 감정 사전으로 음식점 리뷰에 대한 오피니언 마이닝을 시도해 볼 수 있지만, 데이터의 차이로 인해 정확도가 떨어질 수 있다. 따라서 본 논문은 음 식점 리뷰 데이터로 SVM (Support Vector Machine) 모델을 학습 시킨 뒤, 리뷰의 별점을 기준으로 분류하는 방법 으로 오피니언 마이닝을 수행한다. 텍스트로 이루어진 리뷰를 SVM 알고리즘을 적용하기 위해 자연어 처리와 텍스트 마이닝을 통해 수치로 이루어진 데이터로 변환한다. 그리고 별점을 클래스로 간주하여 분류한다. 실제 별점과 분류된 별점의 오차를 확인한 결과, 정확하게 분류된 데이터가 약 63%, 오차의 크기가 1인 데이터가 약 24%, 나머지 데이터가 약 12%로 대부분 알맞게 분류가 되었다. 또한 음식점의 리뷰에서 특수성을 갖는 몇 가지 어휘를 발견할 수 있었다.

Opinion mining using ensemble text hidden Markov models for text classification

Kang, Mangi,Ahn, Jaelim,Lee, Kichun Elsevier 2018 expert systems with applications Vol.94 No.-

<P><B>Abstract</B></P> <P>With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Proposed a new sentiment analysis method, based on text-based hidden Markov models, that uses word orders without the need of sentiment lexicons. </LI> <LI> Proposed an ensemble of text-based hidden Markov models using boosting and clusters of words produced by latent semantic analysis. </LI> <LI> Showed the method has potential to classify implicit opinions by the proposed ensemble method. </LI> <LI> Showed better performance in comparison to several previous algorithms in several datasets. </LI> <LI> Applied it to a real-life dataset to classify paper titles. </LI> </UL> </P>

Opinion-Mining Methodology for Social Media Analytics

( Yoosin Kim ),( Seung Ryul Jeong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.1

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers` opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

FEROM: Feature Extraction and Refinement for Opinion Mining

Hana Jeong,Dongwook Shin,최중민 한국전자통신연구원 2011 ETRI Journal Vol.33 No.5

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

단어패턴 빈도를 이용한 단문 오피니언 문서 분류기법의 실험적 평가

장재영,김일민 한국인터넷방송통신학회 2012 한국인터넷방송통신학회 논문지 Vol.12 No.5

데이터 마이닝의 문서분류 기술에서 발전된 오피니언 마이닝은 이제 국외뿐만 아니라 국내 산업에서 중요한 관심분야로 자리잡아가고 있다. 오피니언 마이닝의 핵심은 문서에서 감정 단어를 추출하여 긍정/부정 여부를 얼마나 정확하게 판별하느냐를 평가하는 것이다. 국내에서도 이에 관련된 많은 연구가 이루어 졌으나 아직 실용적으로 적용 할 만큼의 분류 정확도를 보이지 않고 있다. 한국어의 경우 비문법적 표현, 감정단어의 다양성 등으로 인해 문서의 극성을 판별하기가 쉽지 않기 때문이다. 본 논문에서는 문법적 요소를 최대한 배제하고 단어패턴의 빈도만을 고려한 새로운 오피니언 문서 분류기법을 제안한다. 제안된 방법에서는 문서를 단어들의 리스트로 추상화한 후, 패턴들의 빈 도를 이용하여 기계학습 알고리즘을 적용한다. 이후에 적절한 스코어 함수를 적용하여 문서의 극성을 판별한다. 또한 제안된 기법의 정확도를 평가하기 위해서 실험결과를 제시한다. 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.

Search Ranking Utilizing User’s Opinion

Jun Jin Choong,Jer Lang Hong 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.7

The Internet is one of the most widely available services in the world today. With the Internet, people are now looking for reviews on the Internet; more specifically, the social networking services. Within the social network medium, we can identify a suitable service that describes more about a person’s personality as the subject. The growth of social networking popularity has contributed to the in-crease in information available on social networking services. The flexibility of these services allows writing individual thoughts without restrictions. With the vast information available on social networking sites today, how is it possible to look all of these opinions? How do we know which opinion holds truth? How do we know if someone is not bias based on his writing? Hence, it is seen necessary to filter opinions. In this paper we look at the possibility of using search ranking as a medium of filter opinions by exploring opinion mining methods, social net-working candidates and search ranking methods. With existing sentiment analysis techniques, we can obtain opinions that are then ranked against a set of key-words.