Discovering Community Preference Influence Network by Social Network Opinion Posts Mining View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Tamanna S. Mumu , Christie I. Ezeife

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. More... »

PAGES

136-145

Book

TITLE

Data Warehousing and Knowledge Discovery

ISBN

978-3-319-10159-0
978-3-319-10160-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10160-6_13

DOI

http://dx.doi.org/10.1007/978-3-319-10160-6_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019287262


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