Discovering Community Preference Influence Network by Social Network Opinion Posts Mining
Document Type
Conference Paper
Publication Date
9-2-2014
Publication Title
International Conference on Data Warehousing and Knowledge Discovery
First Page
136
Last Page
145
Abstract
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. This paper proposes a new influence network (IN) generation algorithm (Opinion Based IN:OBIN) through opinion mining of friendship networks. 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.
DOI
10.1007/978-3-319-10160-6_13
Recommended Citation
Mumu, Tamanna S. and Ezeife, C. I.. (2014). Discovering Community Preference Influence Network by Social Network Opinion Posts Mining. International Conference on Data Warehousing and Knowledge Discovery, 136-145.
https://scholar.uwindsor.ca/computersciencepub/52