Earticle

현재 위치 Home

Automatic Construction of Movie Domain Korean Sentiment Dictionary Using Online Movie Reviews

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJSEIA) 바로가기
  • 간행물
    International Journal of Software Engineering and Its Applications SCOPUS 바로가기
  • 통권
    Vol.9 No.2 (2015.02)바로가기
  • 페이지
    pp.251-260
  • 저자
    Heeryon Cho, Sang-Hyun Choi
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A242024

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

원문정보

초록

영어
We present a method of automatically constructing a domain-specific Korean sentiment dictionary which can be used to classify the sentiment of online movie reviews. More than 1.18 million online movie reviews with movie ratings ranging between 1 to 4 and 7 to 10 were collected across fourteen different movie genres to calculate the joint probability of a given word and the sentiment of movie reviews for each genre. In particular, the joint probability of (1) a given word and the positive movie reviews that contain movie ratings 7 to 10 and (2) a given word and the negative movie reviews that contain movie ratings 1 to 4 for each movie genre were calculated. The difference between the two joint probabilities (i.e., (1) – (2)) was obtained for each word in each genre, and the fourteen genres’ joint probability differences of each word were averaged. Finally, the averaged joint probability difference values were normalized to range between -1 and 1. These normalized values were utilized as the sentiment values of each word in the final 135,082-word movie domain Korean sentiment dictionary. The positive/negative binary sentiment classification performance of the constructed sentiment dictionary was evaluated using test data, and the balanced accuracy of 80.7% was achieved, confirming the effectiveness of the proposed sentiment dictionary construction method.

목차

Abstract
 1. Introduction
 2. Existing Approaches
 3. Sentiment Dictionary Construction
  3.1. Data
  3.2. Method and Assumption
  3.3. Implementation
 4. Experiment
  4.1. Parameter Setting and Test Data
  4.2. Evaluation Measure
  4.3. Threshold Setting
  4.4. Result
 5. Conclusion
 Acknowledgements
 References

키워드

Korean Sentiment Dictionary Online Movie Reviews Sentiment Classification

저자

  • Heeryon Cho [ BK21Plus Big Data Service Model Optimization Team Department of Management Information Systems Chungbuk National University, South Korea ]
  • Sang-Hyun Choi [ BK21Plus Big Data Service Model Optimization Team Department of Management Information Systems Chungbuk National University, South Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Software Engineering and Its Applications
  • 간기
    월간
  • pISSN
    1738-9984
  • 수록기간
    2008~2016
  • 등재여부
    SCOPUS
  • 십진분류
    KDC 505 DDC 605

이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.9 No.2

    피인용수 : 0(자료제공 : 네이버학술정보)

    함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.

      페이지 저장