• Title/Summary/Keyword: 감성 자질

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Extracting Core Events Based on Timeline and Retweet Analysis in Twitter Corpus (트위터 문서에서 시간 및 리트윗 분석을 통한 핵심 사건 추출)

  • Tsolmon, Bayar;Lee, Kyung-Soon
    • KIPS Transactions on Software and Data Engineering
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    • v.1 no.1
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    • pp.69-74
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    • 2012
  • Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

Extracting Core Event Feature Based on Timeline Analysis and Sentiment Feature in Twitter Corpus (트위터 자료의 시간별 분석과 감성 자질을 이용한 핵심 사건 추출)

  • Kim, Hui-Hwan;Tsolmon, Bayar;Lee, Kyung-Soon
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.395-398
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    • 2011
  • 트위터 사용자들은 어떠한 이슈에 대해 트위터를 통해 빠르고 간결하게 다른 사람들과의 지속적인 커뮤니케이션을 원하고, 이러한 특징은 이슈 별 사건에 따라 트윗 개수에 영향을 미치게 된다. 만약 어느 하나의 사회적 이슈에 대해 어떠한 사건이 일어나게 되면 그때의 트윗 개수는 폭발적으로 증가하게 된다. 본 논문에서는 이러한 특징을 이용하여 트위터 자료를 시간별로 분석하여 사건을 인식하고, 감성 자질과 카이제곱 값을 이용해 해당 날짜에 대한 핵심 사건을 추출한다.

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Sentiment Classification of Movie Reviews using Levenshtein Distance (Levenshtein 거리를 이용한 영화평 감성 분류)

  • Ahn, Kwang-Mo;Kim, Yun-Suk;Kim, Young-Hoon;Seo, Young-Hoon
    • Journal of Digital Contents Society
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    • v.14 no.4
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    • pp.581-587
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    • 2013
  • In this paper, we propose a method of sentiment classification which uses Levenshtein distance. We generate BOW(Bag-Of-Word) applying Levenshtein daistance in sentiment features and used it as the training set. Then the machine learning algorithms we used were SVMs(Support Vector Machines) and NB(Naive Bayes). As the data set, we gather 2,385 reviews of movies from an online movie community (Daum movie service). From the collected reviews, we pick sentiment words up manually and sorted 778 words. In the experiment, we perform the machine learning using previously generated BOW which was applied Levenshtein distance in sentiment words and then we evaluate the performance of classifier by a method, 10-fold-cross validation. As the result of evaluation, we got 85.46% using Multinomial Naive Bayes as the accuracy when the Levenshtein distance was 3. According to the result of the experiment, we proved that it is less affected to performance of the classification in spelling errors in documents.

A Study on Social Media Sentiment Analysis for Exploring Public Opinions Related to Education Policies (교육정책관련 여론탐색을 위한 소셜미디어 감정분석 연구)

  • Chung, Jin-Myeong;Yoo, Ki-Young;Koo, Chan-Dong
    • 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.

A Comparative Study on Using SentiWordNet for English Twitter Sentiment Analysis (영어 트위터 감성 분석을 위한 SentiWordNet 활용 기법 비교)

  • Kang, In-Su
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.4
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    • pp.317-324
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    • 2013
  • Twitter sentiment analysis is to classify a tweet (message) into positive and negative sentiment class. This study deals with SentiWordNet(SWN)-based twitter sentiment analysis. SWN is a sentiment dictionary in which each sense of an English word has a positive and negative sentimental strength. There has been a variety of SWN-based sentiment feature extraction methods which typically first determine the sentiment orientation (SO) of a term in a document and then decide SO of the document from such terms' SO values. For example, for SO of a term, some calculated the maximum or average of sentiment scores of its senses, and others computed the average of the difference of positive and negative sentiment scores. For SO of a document, many researchers employ the maximum or average of terms' SO values. In addition, the above procedure may be applied to the whole set (adjective, adverb, noun, and verb) of parts-of-speech or its subset. This work provides a comparative study on SWN-based sentiment feature extraction schemes with performance evaluation on a well-known twitter dataset.