• 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.

• Design Convergence Study
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• v.15 no.2
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• pp.347-368
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• 2016

Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.

• The Journal of Bigdata
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• v.1 no.1
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• pp.1-7
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• 2016

Social media such as twitter has been popular by the diffusion of internet, and thanks to the radical improvement of computational ability of computers big data analysis became possible. This research is regarding about smart watch which is receiving attention as post-smartphone technology. Among various types of smart watch, this research focuses on the recently released Samsung Galaxy Gear S2. The main purpose of the research is to analyze customer's actual twitter data that was produced before and after the release of the smart watch to the market. Through the analysis, this research provides practical marketing strategy guideline, and also the analysis framework used in this research can be a research framework for other area and product researches.

• Asian Journal for Public Opinion Research
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• v.5 no.4
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• pp.266-284
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• 2018

This paper adopts the big data research method, and draws 491 data from the Tianya Forum about the Simultaneous Renting and Purchasing policy of Guangzhou. The qualitative analysis software Nvivo11 is used to cluster the main questions about the Simultaneous Renting and Purchasing policy in the forum. The 36 high-frequency word frequencies are obtained through text clustering. Through rooted theory analysis, the main driving factors for summarizing people's doubts are 9 main categories, 3 core categories, and the model of driving factors for online forums is established. The study finds that resource factors are the most key factor, economic factors are the important drivers, and policy guiding factors are sub-important drivers.

• Korean Journal of Cognitive Science
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• v.23 no.1
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• pp.97-107
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• 2012

Previous studies on opinion mining and sentiment analysis have supposed that the polarity and value of an emotional word is reversed when a negation word is attached. However, there are no quantitative studies on how much the polarity is changed when a negation word is following. Therefore, we measured the valence and arousal dimensions for Korean emotional words and their negations. Consequently, the polarity of valence and arousal was reversed on their intermediate level. Also, the value was reduced by about 30% to 50%. We propose this result as a guideline for processing negation words for studies on opinion mining and sentiment analysis.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• The Journal of Information Systems
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• v.32 no.3
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• pp.151-164
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• 2023

Purpose This study aims to analyze the current status of consumer awareness on Internet banks by conducting a full investigation and collecting user opinions presented on Google Play. After cateogorizing the current dissatisfaction, we would like to present not only the direction of the Internet bank service of but also the improvements of the platform. Design/methodology/approach Using opinion mining, subjectivity analysis, polarity analysis, and polarity information analysis of comments were conducted step by step to extract negative and positive keywords. The extracted keywords analyzed the weights of the frequently appearing positive and negative keywords using the TF-IDF model. Based on previous studies that negative information is more sensitive to positive information, we tried to confirm the connection, proximity, and mediation of negative keywords. Semantic Network Analysis (SNA) was used to visualize the connection relationship between the negative comment keywords of the three Internet banks. Findings Domestic Internet banks such as Kakao Bank, K-Bank, and Toss Bank have attracted a lot of attention even before they were established, and after establishment, they have secured a wide range of users through platforms that are completely different from existing banks. This study found out that the convenience of the app affects the opening and transaction of non-face-to-face accounts, which are characteristics of domestic Internet banks, which also affects the bank's business strategy. In addition, this study shows that the business characteristics of the company can be identified.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.33-42
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• 2016

Recently, an environment in which public opinions are expressed about various areas is expanded around SNSs or internet potals, thus, opinion documents get bigger rapidly. Under these circumstances, it is essential to utilize automatic summarization techniques for understanding whole contents of large opinion documents. However, it is hard to summarize efficiently those documents with traditional text summarization technologies since the documents include subject expressions as well as features of targets objects. Proposed method in this paper defines features of opinion documents, and designed to retrieve representative sentences expressing opinions of those features. In addition, through experiments, we prove the usefulness of proposed method.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.240-245
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• 2016

In this study, comments on restaurants, movies, and mobile devices, as well as tweet messages regardless of specific domains were analyzed for sentimental information content. We proposed a system for extraction of objects (or aspects) and opinion words from each sentence and the subsequent evaluation. For the sentiment analysis, we conducted a comparative evaluation between the Structural SVM algorithm and the Latent Structural SVM. As a result, the latter showed better performance and was able to extract objects/aspects and opinion words using VP/NP analyzed by the dependency parser tree. Lastly, we also developed and evaluated the sentiment detector model for use in practical services.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.