• Title, Summary, Keyword: 감성 자질

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Effective Korean sentiment classification method using word2vec and ensemble classifier (Word2vec과 앙상블 분류기를 사용한 효율적 한국어 감성 분류 방안)

  • Park, Sung Soo;Lee, Kun Chang
    • Journal of Digital Contents Society
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    • v.19 no.1
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    • pp.133-140
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    • 2018
  • Accurate sentiment classification is an important research topic in sentiment analysis. This study suggests an efficient classification method of Korean sentiment using word2vec and ensemble methods which have been recently studied variously. For the 200,000 Korean movie review texts, we generate a POS-based BOW feature and a feature using word2vec, and integrated features of two feature representation. We used a single classifier of Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine and an ensemble classifier of Adaptive Boost, Bagging, Gradient Boosting, and Random Forest for sentiment classification. As a result of this study, the integrated feature representation composed of BOW feature including adjective and adverb and word2vec feature showed the highest sentiment classification accuracy. Empirical results show that SVM, a single classifier, has the highest performance but ensemble classifiers show similar or slightly lower performance than the single classifier.

Building Korean Multi-word Expression Lexicons and Grammars Represented by Finite-State Graphs for FbSA of Cosmetic Reviews (화장품 후기글의 자질기반 감성분석을 위한 다단어 표현의 유한그래프 사전 및 문법 구축)

  • Hwang, Chang-Hoe;Yoo, Gwang-Hoon;Choi, Seong-Yong;Shin, Dong-Heouk;Nam, Jee-Sun
    • Annual Conference on Human and Language Technology
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    • pp.400-405
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    • 2018
  • 본 연구는 한국어 화장품 리뷰 코퍼스의 자질기반 감성 분석을 위하여, 이 도메인에서 실현되는 중요한 다단어 표현(MWE)의 유한상태 그래프 사전과 문법을 구축하는 방법론을 제시하고, 실제 구축된 사전과 문법의 성능을 평가하는 것을 목표로 한다. 본 연구에서는 자연어처리(NLP)에서 중요한 화두로 논의되어 온 MWE의 어휘-통사적 특징을 부분문법 그래프(LGG)로 형식화하였다. 화장품 리뷰 코퍼스에 DECO 한국어 전자사전을 적용하여 어휘 빈도 통계를 획득하고 이에 대한 언어학적 분석을 통해 극성 MWE(Polarity-MWE)와 화제 MWE(Topic MWE)의 전체 네 가지 하위 범주를 분류하였다. 또한 각 모듈간의 상호관계에 대한 어휘-통사적 속성을 반복적으로 적용하는 이중 증식(double-propagation)을 통해 자원을 확장하였다. 이 과정을 통해 구축된 대용량 MWE 유한그래프 사전 DECO-MWE의 성능을 테스트한 결과 각각 0.844(Pol-MWE), 0.742(Top-MWE)의 조화평균을 보였다. 이를 통해 본 연구에서 제안하는 MWE 언어자원 구축 방법론이 다양한 도메인에서 활용될 수 있고 향후 자질기반 감성 분석에 중요한 자원이 될 것임을 확인하였다.

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Designing SNS tourism review rating system through learning of scored review text (평점이 포함된 문장 학습을 통한 SNS 관광지 리뷰 평점 부여 시스템 설계)

  • An, Hyeon Woo;Moon, Nammee
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.739-741
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    • 2018
  • 감성분석을 통한 텍스트의 긍/부정 판단은 의사결정 시스템을 포함한 여러 분야에서 중요한 역할을 맡고 있다. 이런 흐름에 맞춰 감성분석 기술은 여러 기술과 융합하여 발전해왔는데 문장 내 자질을 추출하여 이용하는 자질 공학적 접근 방식과 심층 신뢰 신경망을 이용한 구조 또한 응용 사례에 속한다. 본 논문에서는 이러한 응용 기술 중 심층 신경망을 응용한 분석기술을 사용하여 관광지에 대한 평점이 포함된 문장을 학습하고 이를 SNS 관광지 리뷰에 적용하여 평점을 매기는 시스템을 설계한다.

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.

Sentiment Analysis System Using Stanford Sentiment Treebank (스탠포드 감성 트리 말뭉치를 이용한 감성 분류 시스템)

  • Lee, Songwook
    • Journal of Advanced Marine Engineering and Technology
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    • v.39 no.3
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    • pp.274-279
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    • 2015
  • The main goal of this research is to build a sentiment analysis system which automatically determines user opinions of the Stanford Sentiment Treebank in terms of three sentiments such as positive, negative, and neutral. Firstly, sentiment sentences are POS tagged and parsed to dependency structures. All nodes of the Treebank and their polarities are automatically extracted from the Treebank. We train two Support Vector Machines models. One is for a node level classification and the other is for a sentence level. We have tried various type of features such as word lexicons, POS tags, Sentiment lexicons, head-modifier relations, and sibling relations. Though we acquired 74.2% in accuracy on the test set for 3 class node level classification and 67.0% for 3 class sentence level classification, our experimental results for 2 class classification are comparable to those of the state of art system using the same corpus.

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.

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.

Optimal supervised LSA method using selective feature dimension reduction (선택적 자질 차원 축소를 이용한 최적의 지도적 LSA 방법)

  • Kim, Jung-Ho;Kim, Myung-Kyu;Cha, Myung-Hoon;In, Joo-Ho;Chae, Soo-Hoan
    • Science of Emotion and Sensibility
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    • v.13 no.1
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    • pp.47-60
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    • 2010
  • Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

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A Study of the Effects of the Self-Emotional Ability and Social-Emotional Ability on the Teamwork Capability of the Airline Flight Attendants (항공사 객실승무원의 개인적 감성능력과 상회적 감성능력이 팀웍역량에 미치는 영향에 관한 연구)

  • Chung, Min-Joo;Chang, Dae-Sung
    • Journal of Advanced Navigation Technology
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    • v.16 no.2
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    • pp.318-329
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    • 2012
  • Flight attendants are the most important people which decides customer's satisfaction and service quality toward the airline service. Nowadays the service employees' emotional ability over self and others are considered to be a important service competence. And flight attendants' job performance are achieved in their team system. The purpose of this study is to examine how the service employees' emotional awareness and management abilities can affect their teamwork capability. This study was conducted through literature and empirical methods, and collected questionnaire was analyzed employing SPSS version 15.0 statistics package and AMOS 17.0. The result of this study was found that flight attendants who have high self-emotional ability and social-emotional ability show higher teamwork capability in their flights. It is meaningful because it expanded the range of the researches about the emotional ability and proposed new substructure of researches on the service human resources.