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TipMe: Personalized advertising and aspect-based opinion mining for users and businesses
Someris: Social Media and Risk ASONAM 2015 Workshop (2015)
  • Dimitris Proios
  • Magdalini Eirinaki, San Jose State University
  • Iraklis Varlamis
Abstract
Online advertisements are a major source of profit and customer attraction for web-based businesses. In a successful advertisement campaign, both users and businesses can benefit, as users are expected to respond positively to special offers and recommendations of their liking and businesses are able to reach the most promising potential customers. The extraction of user preferences from content provided in social media and especially in review sites can be a valuable tool both for users and businesses. In this paper, we propose a model for the analysis of content from product review sites, which considers in tandem the aspects discussed by users and the opinions associated with each aspect. The model provides two different visualizations: one for businesses that uncovers their weak and strong points against their competitors and one for end-users who receive suggestions about products of potential interest. The former is an aggregation of aspect-based opinions provided by all users and the latter is a collaborative filtering approach, which calculates user similarity over a projection of the original bipartite graph (user-item rating graph) over a content-based clustering of users and items. The model takes advantage of the feedback users give to businesses in review sites, and employ opinion mining techniques to identify the opinions of users for specific aspects of a business. Such aspects and their polarity can be used to create user and business profiles, which can subsequently be fed in a clustering and recommendation process. We envision this model as a powerful tool for planning and executing a successful marketing campaign via online media. Finally, we demonstrate how our prototype can be used in different scenarios to assist users or business owners, using the Yelp challenge dataset.
Keywords
  • advertising,
  • personalization,
  • targeted ads,
  • recommender,
  • Yelp
Publication Date
August, 2015
Citation Information
Dimitris Proios, Magdalini Eirinaki and Iraklis Varlamis. "TipMe: Personalized advertising and aspect-based opinion mining for users and businesses" Someris: Social Media and Risk ASONAM 2015 Workshop (2015)
Available at: http://works.bepress.com/magdalini_eirinaki/37/