首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0204113-123022


URN etd-0204113-123022 Statistics This thesis had been viewed 1053 times. Download 0 times.
Author Chia-Hung Shen
Author's Email Address No Public.
Department Information Management
Year 2012 Semester 1
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 52
Title ONLINE COMMUNITY COMMENTS RANKING SYSTEM : TAKING FACEBOOK AS AN EXAMPLE
Keyword
  • Support Vector Machine (SVM)
  • Facebook
  • Kappa
  • Lesk Algorithm
  • rank
  • Social network
  • Social network
  • rank
  • Lesk Algorithm
  • Kappa
  • Facebook
  • Support Vector Machine (SVM)
  • Abstract With the wide application and diverse access equipment of online community in recent years, people can easily browse and respond to online information, including photography, text, video and etc. However, a large part of text response, especially in micro-blogs, contains no essential meanings except the speaker. Half or more of them are unmeaning words such as greeting, advertising, murmurs, and irrelative arguments. Therefore, people cannot catch the main point of response in short time, and the subject will no longer be concerned and discussed.
    For improving this, this study proposes an evaluate system for comments on Facebook’s wall posts, and ranking the importance of them. It can help user reading comments with high ranks directly and improve the quality of comments follow-up.
    In this study, we classify all comments into 2 sets: (1) relative and (2) irrelative on every stage of : (1) Semantic analysis, (2) Meta-data features compare, and (3) Corpus similarity computing. Base on the result, we get True Positive Rate (TPR) as high as 0.9 for False Positive Rate (FPR) = 0.1.
    Advisor Committee
  • Huei-Huang Chen - advisor
  • Files indicate in-campus access at 5 years and off-campus not accessible
    Date of Defense 2013-01-09 Date of Submission 2013-02-04


    Browse | Search All Available ETDs