• International Journal of Knowledge Content Development & Technology
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• v.12 no.4
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• pp.41-65
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• 2022

This study would understand the overall perception of our society about public libraries, analyzing the texts related to public libraries, utilizing the semantic connection network & sentiment analysis. For this purpose, this study collected data from the last five years with keywords, 'Library' and 'Lifelong Learning Center' from January 1, 2016 through November 30, 2020 through the blogs and cafés of major domestic portal sites. With the collected data, text mining, centrality of keywords, network structure, structural equipotentiality, and sensitivity analyses were conducted. As a result of the analysis, First, 'reading' and 'book' were identified as representative keywords that form the social perception of public libraries. Second, it turned out that there were keywords related to the use of the library and the untact service due to the recent spread of COVID-19. Third, in seeking a plan for the development of public libraries through the keywords drawn to have positive meanings, it is necessary to create continuous services that can form a new image of the library, breaking away from the existing fixed role and image of the library and increase the convenience of use. Fourth, facilities and facilities for library services were recognized from a neutral point of view. Fifth, the spread of infectious diseases, social distancing, and temporary closure and closure of libraries are negatively related to public libraries, and awareness of librarians has been identified as negative keywords.

• Informatization Policy
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• v.24 no.4
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• pp.3-16
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• 2017

With the development of social media services in the era of Web 2.0, the public opinion formation site has been partially shifted from the traditional mass media to social media. This phenomenon is continuing to expand, and public opinions on government polices created and shared on social media are attracting more attention. It is particularly important to grasp public opinions in policy formulation because setting up educational policies involves a variety of stakeholders and conflicts. The purpose of this study is to explore public opinions about education-related policies through an empirical analysis of social media documents on education policies using opinion mining techniques. For this purpose, we collected the education policy-related documents by keyword, which were produced by users through the social media service, tokenized and extracted sentimental qualities of the documents, and scored the qualities using sentiment dictionaries to find out public preferences for specific education policies. As a result, a lot of negative public opinions were found regarding the smart education policies that use the keywords of digital textbooks and e-learning; while the software education policies using coding education and computer thinking as the keywords had more positive opinions. In addition, the general policies having the keywords of free school terms and creative personality education showed more negative public opinions. As much as 20% of the documents were unable to extract sentiments from, signifying that there are still a certain share of blog posts or tweets that do not reflect the writers' opinions.

• Knowledge Management Research
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• v.22 no.4
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• pp.217-235
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• 2021

As 49% of the world's population uses social media platforms, communication and content sharing within social media are becoming more active than ever. In this environmental base, the one-person media market grew rapidly and formed public opinion, creating a new trend called sell-sumer. This study defined new types of influencers by product category by analyzing the subject concentration of the commercial/non-commercial keywords of influencers and the impact of the ratio of commercial postings on sales. It is hoped that influencers working within social media will be helpful to new sales strategies that are transformed into sell-sumers. The method of this study classifies influencers' commercial/non-commercial posts using Python, performs text mining using KoNLPy, and calculates similarity between FastText-based words. As a result, it has been confirmed that the higher the keyword theme concentration of the influencer's commercial posting, the higher the sales. In addition, it was confirmed through the cluster analysis that the influencer types for each product category were classified into four types and that there was a significant difference between groups according to sales. In other words, the implications of this study may suggest empirical solutions of social media sales strategies for influencers working on social media and marketers who want to use them as marketing tools.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.311-321
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• 2024

This study comprehensively analyzed the perceptions of Chinese consumers who have and have not purchased Samsung smartphones, based on data from the social media platform Weibo. Various big data analysis techniques were used, including text mining, frequency analysis, centrality analysis, semantic network analysis, and CONCOR analysis. The results indicate that positive perceptions of Samsung smartphones include aspects such as design aesthetics, camera functionality, AI features, screen quality, specifications, and performance, and their status as a premium brand. On the other hand, negative perceptions include issues with pricing, a yellow tint in photos, slow charging speeds, and safety concerns. These findings will provide a crucial basis for making significant improvements in Samsung's market strategy in China.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.279-284
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• 2017

Currenty, in the era of big data, text mining and opinion mining have been used in many domains, and one of their most important research issues is to extract significant information from social media. Thus in this paper, we propose a logistic regression ensemble method of finding the main body text from blog HTML. First, we extract structural features and text features from blog HTML tags. Then we construct a classification model with logistic regression and ensemble that can decide whether any given tags involve main body text or not. One of our important findings is that the main body text can be found through 'depth' features extracted from HTML tags. In our experiment using diverse topics of blog data collected from the web, our tag classification model achieved 99% in terms of accuracy, and it recalled 80.5% of documents that have tags involving the main body text.

• Smart Media Journal
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• v.6 no.1
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• pp.47-53
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• 2017

Social issues are important factors that decide government policy and newspapers are critical channels that reflect them. Analysing news articles can contribute to understanding social issues, but it is very difficult to analyse the unstructured large volumes of news data manually. Therefore, this study aims to analyze the different views among stakeholders of a specific social issue by using text analysis, word cloud analysis and associative analysis methods, which systematically transform unstructured news data into structured one. We analyzed a total of 115 news articles and a total of 6,772 comments, collected from the selected newspapers (Chosun-Il-bo, Joongang-Il-bo, Donga-Il-bo, Maeil Newspaper, Busan-Il-bo) for two weeks. We found that there are significant differences in tone between newspapers. While nation-wide daily newspapers focus on political relations with local areas, local daily newspapers tend to write articles to represent local governments' interests.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.53-57
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• 2012

본 논문은 소셜 미디어 기반 의사결정 지원 시스템인 '소셜위즈덤'에 포함된 이벤트 템플릿 추출에 대해서 소개한다. 의사결정 지원 시스템은 경제적, 사회적 중요사항을 결정할 수 있도록 관련 정보와 인사이트(Insight)를 제공하는 정보시스템을 이른다. 기존 시스템은 단지 특정 키워드 빈도나 공기하는 키워드들의 관계만을 제공하였다. 그러나, 소셜위즈덤은 이벤트로 정의되는 주체(Subject), 이벤트 속성(Event-Property), 객체(Object)의 트리플(Triple) 집합인 템플릿을 추출하여 이를 기반으로 이벤트 정보를 함께 제공한다. 템플릿 추출은 고정밀 언어분석의 관계추출 기술과 온톨로지에 기반한 템플릿 제약 및 필터링 규칙을 이용하였다. 수작업으로 구축한 평가데이터로 평가한 결과, 템플릿 추출 성능(F-Score)은 뉴스 0.544, 블로그 0.3386, 트위터 0.3251이고 전체 통합 성능은 0.4648이었다. 필터링 성능(Accuracy)은 뉴스 0.7257, 블로그 0.6122, 트위터 0.6207이고 전체 통합 성능은 0.722이었다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.149-156
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• 2021

Text mining is one of the useful tools to discover public opinion and perception regarding political issues from big data. It is very common that users of social media express their opinion with newly-coined words such as slang and emoji. However, those new words are not effectively captured by traditional text mining methods that process text data using a language dictionary. In this study, we propose effective methods to extract newly-coined words that connote the political stance and opinion of users. With various text mining techniques, I attempt to discover the context and the political meaning of the new words.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.97-104
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• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.