Social Network Opinion and Posts Mining for Community Preference Discovery

详细信息   

• 作者：Mumu ; Tamanna
• 学历：Master
• 年：2013
• 关键词：Communication and the arts ; Applied sciences ; Influe
• 导师：Ezeife,Christie I.,Kobti,Ziad
• 毕业院校：University of Windsor
• Department：COMPUTER SCIENCE
• 专业：Web Studies;Information science;Computer science
• ISBN：9780494871249
• CBH：MR87124
• Country：U.K.
• 语种：English
• FileSize：1271816
• Pages：114

文摘

The popularity of posts,topics,and opinions on social media websites and the influence ability of users can be discovered by analyzing the responses of users (e.g.,likes/dislikes,comments,ratings). Existing web opinion mining systems such as OpinionMiner is based on opinion text similarity scoring of users' review texts and product ratings to generate database table of features,functions and opinions mined through classification to identify arriving opinions as positive or negative on user-service networks or interest networks (e.g.,Amazon.com). These systems are not directly applicable to user-user networks or friendship networks (e.g.,Facebook.com) since they do not consider multiple posts on multiple products,users' relationships (such as influence),and diverse posts and comments. In this thesis,we propose a new influence network (IN) generation algorithm (Opinion Based IN:OBIN) through opinion mining of friendship networks (like Facebook.com). OBIN mines opinions using extended OpinionMiner that considers multiple posts and relationships (influences) between users. Approach used includes frequent pattern mining algorithm for determining community (positive or negative) preferences for a given product as input to standard influence maximization algorithms like CELF for target marketing. Experiments and evaluations show the effectiveness of OBIN over CELF in large-scale friendship networks. KEYWORDS Influence Analysis,Recommendation,Ranking,Sentiment Classification,Large Scale Network,Social Network,Opinion Mining,Text Mining.