The analysis of social networking sites is a vast area of research as there are tremendous measures of records showing up in online networking. Predicting the comment patterns of users on these sites is a complex decision making process. This paper proposes a hybrid model of linear regression (PACE regression) and non linear regression (REP Tree) that predicts the likelihood of the comment volume, which a post may receive by analyzing the various features of the corresponding page, post and previous records of comment patterns of users. To mechanize the procedure, a model is built comprising of the crawler, data processor and information revelation module. The new hybridized model has improved the time and space complexity along with Accuracy by building a right sized tree using only significant features with low misclassification rate.
목차
Abstract 1. Introduction 2. Related Work 3. Comment Volume Prediction 3.1. Problem Formulation 4. Comment Volume Prediction 4.1. Evaluation Metrics 4.2. Results 4. Conclusion References
키워드
Social mediaComment volume predictionPACE regressionREP tree
저자
Mandeep Kaur [ Dept. of Computer Science engg.CTIEMT Jalandhar, Punjab, India ]
Prince Verma [ Dept. of Computer Science engg.CTIEMT Jalandhar, Punjab, India ]
보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
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 Database Theory and Application
간기
격월간
pISSN
2005-4270
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.9 No.11