• Title, Summary, Keyword: Opinion Mining

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FEROM: Feature Extraction and Refinement for Opinion Mining

  • Jeong, Ha-Na;Shin, Dong-Wook;Choi, Joong-Min
    • ETRI Journal
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    • v.33 no.5
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    • pp.720-730
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    • 2011
  • 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.

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.

Design and Implementation of Opinion Mining System based on Association Model (연관성 모델에 기반한 오피년마이닝 시스템의 설계 및 구현)

  • Kim, Keun-Hyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.1
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    • pp.133-140
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    • 2011
  • For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

Design And Implementation of a Speech Recognition Interview Model based-on Opinion Mining Algorithm (오피니언 마이닝 알고리즘 기반 음성인식 인터뷰 모델의 설계 및 구현)

  • Kim, Kyu-Ho;Kim, Hee-Min;Lee, Ki-Young;Lim, Myung-Jae;Kim, Jeong-Lae
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.225-230
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    • 2012
  • The opinion mining is that to use the existing data mining technology also uploaded blog to web, to use product comment, the opinion mining can extract the author's opinion therefore it not judge text's subject, only judge subject's emotion. In this paper, published opinion mining algorithms and the text using speech recognition API for non-voice data to judge the emotions suggested. The system is open and the Subject associated with Google Voice Recognition API sunwihwa algorithm, the algorithm determines the polarity through improved design, based on this interview, speech recognition, which implements the model.

A Design of SNS Emotional Information Analysis Strategy based on Opinion Mining (오피니언 마이닝 기반 SNS 감성 정보 분석 전략 설계)

  • Jeong, Eun-Hee;Lee, Byung-Kwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.8 no.6
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    • pp.544-550
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    • 2015
  • The opinion mining technique which analogize significant information from SNS message is increasingly important because opinions communicated through SNS are increasing. This paper propose SEIAS(SNS Emotional Information Analysis Strategy) based on opinion mining that analogize emotional information from SNS setting a different weight according to position of antonym and adverb. Firstly, the proposed SEIAS constructs a emotion dictionary for opinion mining analysis, Secondly, it collects SNS data on real time, compare it with emotion dictionary and calculates opinion value of SNS data. Specially, it increases the precision of opinion analysis result compared to the existing SO-PMI because it sets up the different value according to the position of antonym and adverb when it calculates the opinion value of data.

Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

  • Eo, Kyun Sun;Lee, Kun Chang
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.2
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    • pp.171-177
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    • 2019
  • This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

Comparison of the Center for Children's Foodservice Management in 2012, 2014, and 2016 Using Big Data and Opinion Mining (2012년, 2014년과 2016년의 어린이급식관리지원센터에 대한 빅데이터와 오피니언 마이닝을 통한 비교)

  • Jung, Eun-Jin;Chang, Un-Jae
    • Journal of the Korean Dietetic Association
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    • v.23 no.2
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    • pp.192-201
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    • 2017
  • This study compared the Center for Children's Foodservice Management in 2012, 2014, and 2016 using big data and opinion mining. The data on the Center for Children's Foodservice Management were collected from the portal site, Naver, from January 1 to December 31 in 2012, 2014, & 2016 and analyzed by keyword frequency analysis, influx route analysis of data, polarity analysis via opinion mining, and positive and negative keyword analysis by polarity analysis. The results showed that nursery had the highest rank every year and education supported by Center for Children's Foodservice Management has increased significantly. The influx of data has increased through the influx route analysis of data. Blog and $caf\acute{e}e$, which have a considerable amount of information by the mother should be helpful for use as public relations and participation recruitment paths. By polarity analysis using opinion mining, the positive image of the Center for Children's Foodservice Management was increased. Therefore, the Center for Children's Foodservice Management was well-suited to the purpose and the interests of the people has been increasing steadily. In the near future, the Center for Children's Foodservice Management is expected have good recognition if various programs to participate with family are developed and advertised.

Opinion-Mining Methodology for Social Media Analytics

  • Kim, Yoosin;Jeong, Seung Ryul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.1
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    • pp.391-406
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    • 2015
  • 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.

Opinion Mining from Internet Article (Opinion Mining을 이용한 신문 기사 사용자 의견 추출)

  • Hwang, Chi hoon;Ryu, Joon suk;Kim, Ung mo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.725-726
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    • 2009
  • 오늘날 인터넷의 발달 때문에 인터넷으로 쉽게 신문을 볼 수 있게 되었다. 또한, 해당 기사에 대한 의견을 사용자끼리 쉽게 교환할 수 있다. 본 논문에서는 이러한 인터넷 기사의 사용자 의견들에 Opinion Mining 기술을 활용하여 해당 기사 대상의 특징을 올바르게 파악하는 방법을 제안한다.

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