NDLI: TipMe: Personalized advertising and aspect-based opinion mining for users and businesses

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TipMe: Personalized advertising and aspect-based opinion mining for users and businesses

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TipMe: Personalized advertising and aspect-based opinion mining for users and businesses

Content Provider	ACM Digital Library
Author	Eirinaki, Magdalini Varlamis, Iraklis Proios, Dimitris
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.
Starting Page	1489
Ending Page	1494
Page Count	6
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2809324
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Targeted ads Recommender Yelp Advertising
Content Type	Text
Resource Type	Article

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