• Title, Summary, Keyword: Sentimental Analysis

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Movie Retrieval System by Analyzing Sentimental Keyword from User's Movie Reviews (사용자 영화평의 감정어휘 분석을 통한 영화검색시스템)

  • Oh, Sung-Ho;Kang, Shin-Jae
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.3
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    • pp.1422-1427
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    • 2013
  • This paper proposed a movie retrieval system based on sentimental keywords extracted from user's movie reviews. At first, sentimental keyword dictionary is manually constructed by applying morphological analysis to user's movie reviews, and then keyword weights in the dictionary are calculated for each movie with TF-IDF. By using these results, the proposed system classify sentimental categories of movies and rank classified movies. Without reading any movie reviews, users can retrieve movies through queries composed by sentimental keywords.

Development and Validation of the Letter-unit based Korean Sentimental Analysis Model Using Convolution Neural Network (회선 신경망을 활용한 자모 단위 한국형 감성 분석 모델 개발 및 검증)

  • Sung, Wonkyung;An, Jaeyoung;Lee, Choong C.
    • The Journal of Society for e-Business Studies
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    • v.25 no.1
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    • pp.13-33
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    • 2020
  • This study proposes a Korean sentimental analysis algorithm that utilizes a letter-unit embedding and convolutional neural networks. Sentimental analysis is a natural language processing technique for subjective data analysis, such as a person's attitude, opinion, and propensity, as shown in the text. Recently, Korean sentimental analysis research has been steadily increased. However, it has failed to use a general-purpose sentimental dictionary and has built-up and used its own sentimental dictionary in each field. The problem with this phenomenon is that it does not conform to the characteristics of Korean. In this study, we have developed a model for analyzing emotions by producing syllable vectors based on the onset, peak, and coda, excluding morphology analysis during the emotional analysis procedure. As a result, we were able to minimize the problem of word learning and the problem of unregistered words, and the accuracy of the model was 88%. The model is less influenced by the unstructured nature of the input data and allows for polarized classification according to the context of the text. We hope that through this developed model will be easier for non-experts who wish to perform Korean sentimental analysis.

Sentimental Analysis of SW Education News Data (SW 교육 뉴스데이터의 감성분석)

  • Park, SunJu
    • Journal of The Korean Association of Information Education
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    • v.21 no.1
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    • pp.89-96
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    • 2017
  • Recently, a number of researches actively focus on the contents and sensitivity of information distributed through SNS as smartphones and SNS gained its popularity. In this paper, we collected online news data about SW education, extracted words after morphological analysis, and analyzed emotions of collected news data by calculating sentimental score of each news datum. Also, the accuracy of the calculated sentimental score was examined. As a result, the number of news related to 'SW education' in the collection period was about 189 per month, and the average of sentimental score was 0.7, which signifies the news related to 'SW education' was emotionally positive. We were positive about the importance of SW education and the policy implementation, but there were negative views on the specific method for the realization. That is, a lack of SW education environment and its education method, a problem related to improvement of SW developers and improvement of their labor conditions, and increase of private education in coding were the factors for the negative viewers.

Informal Quality Data Analysis via Sentimental analysis and Word2vec method (감성분석과 Word2vec을 이용한 비정형 품질 데이터 분석)

  • Lee, Chinuk;Yoo, Kook Hyun;Mun, Byeong Min;Bae, Suk Joo
    • Journal of the Korean Society for Quality Management
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    • v.45 no.1
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    • pp.117-128
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    • 2017
  • Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

Study on the social issue sentiment classification using text mining (텍스트마이닝을 이용한 사회 이슈 찬반 분류에 관한 연구)

  • Kang, Sun-A;Kim, Yoo Sin;Choi, Sang Hyun
    • 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.

Deep learning-based Multilingual Sentimental Analysis using English Review Data (영어 리뷰데이터를 이용한 딥러닝 기반 다국어 감성분석)

  • Sung, Jae-Kyung;Kim, Yung Bok;Kim, Yong-Guk
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.19 no.3
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    • pp.9-15
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    • 2019
  • Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

Favorable analysis of users through the social data analysis based on sentimental analysis (소셜데이터 감성분석을 통한 사용자의 호감도 분석)

  • Lee, Min-gyu;Sohn, Hyo-jung;Seong, Baek-min;Kim, Jong-bae
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.438-440
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    • 2014
  • Recently it is used commercially to actively move the data from the SNS service. Therefore, we propose a method that can accurately analyze the information related to the reputation of companies and products in real time SNS environment in this paper.Identify the relationship between words by performing morphological analysis on the text data gathered by crawling the SNS scheme. In addition, it shows the visualization to analyze statistically through a established emotional dictionary morphemes are extracted from the sentence. Here, if the extracted word is not exist in sentimental dictionary. Also, we propose the algorithm that add the word to emotional dictionary automatically.

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The College Reputation System using Public Data and Sentiment Analysis (공공데이터와 감성분석을 이용한 대학평판시스템)

  • Kim, Eun-Ah;Lee, Yon-Sik
    • Convergence Security Journal
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    • v.18 no.1
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    • pp.103-110
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    • 2018
  • Modern society is increasingly demanding in many areas of big data processing technology to collect, aggregate, and analyze large amounts of data over the Internet and SNS. A typical application is to evaluate the reputation of a company or college. To measure and quantify a reputation, fair and precise data and efficient data processing are very important. For this purpose, a quantitative quotient was obtained using public data, a qualitative quotient was obtained through sentiment analysis using news articles, and a complex college reputation quotient was calculated. In this paper, a complex college reputation quotient was calculated based on the quantitative index, reflecting the sentimental reputation, and based on the proposed mixed university system. In this paper, the Complex College Reputation System(CCRS) was proposed, which produced the Complex College Reputation Quotient with an objective quantitative quotient and qualitative quotient reflecting the sentimental reputation to measure the college reputation.

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User Needs-Based Technology Opportunities in Heterogeneous Fields Using Opinion Mining and Patent Analysis (오피니언 마이닝 및 특허분석을 통한 사용자 니즈기반 이종영역 기술기회 탐색)

  • Jang, Hyejin;Roh, Taeyeoun;Yoon, Byungun
    • Journal of Korean Institute of Industrial Engineers
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    • v.43 no.1
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    • pp.39-48
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    • 2017
  • In a digital economy, users actively express their needs in many ways. Thus, many researchers analyze what users need and whether they are satisfied or not through opinion mining. In addition, they begin to find technology opportunities in heterogeneous technology fields. But they did not connect users' opinion to technology development process, only focused on natural language processing or marketing or manufacturing area. Also, heterogeneous technology fields are focused on fusion technology. Thus, this study suggests a novel approach that is based on sentimental value and can be applied to exploring technology opportunities in heterogeneous fields. Sentimental value is calculated from users' opinion through sLDA. The heterogeneous technology opportunity is explored by patent analysis. This research contributes to suggesting a hybrid methodology through patent and users' opinion. In addition, it can provide managerial efficiency by suggesting base data onto decision making.

Implementation of smart chungbuk tourism based on SNS data analysis (SNS 데이터 분석을 통한 스마트 충북관광 구축)

  • Cho, Wan-Sup;Cho, Ah;Kwon, Kaaen;Yoo, Kwan-Hee
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.2
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    • pp.409-418
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    • 2015
  • With the development of mobile devices and Internet, information exchange has actively been made through SNS and Blogs. Blogs are widely used as a space where people share their experience after their visit to tourist attractions. We propose a method of recommending associated tourist attractions based on tourists' opinions using issue analysis, association analysis, and sentimental analysis for various online reviews including news in order to help to develop tour products and policies. The result shows that north area of Chungbuk province has been selected as issue attractions, and associated attractions/keywards have been identified for given well-known attraction. Positive/negative opinion for review texts has been analyzed and user can grasp the reason for the sentiments. Multidimensional analysis technique has been integrated to derive additional sophisticated insights and various policy proposal for smart tourism.