research-article

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

Authors:

Dimitris Proios,

Magdalini Eirinaki,

Iraklis VarlamisAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1489 - 1494

https://doi.org/10.1145/2808797.2809324

Published: 25 August 2015 Publication History

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.

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Cited By

Lai CPeng P(2023)A Hybrid Deep Learning Method to Extract Multi-features from Reviews and User–Item Relations for Rating PredictionInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00288-516:1Online publication date: 27-Jun-2023
https://doi.org/10.1007/s44196-023-00288-5
Gupta SSandhane R(2022)Use of sentiment analysis in social media campaign design and analysisCARDIOMETRY10.18137/cardiometry.2022.22.351363(351-363)Online publication date: 25-May-2022
https://doi.org/10.18137/cardiometry.2022.22.351363
Alengadan BKhan S(2018)Modified aspect/feature based opinion mining for a product ranking system2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)10.1109/ICCTAC.2018.8370393(1-5)Online publication date: Feb-2018
https://doi.org/10.1109/ICCTAC.2018.8370393
Show More Cited By

TipMe: Personalized advertising and aspect-based opinion mining for users and businesses
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Lai CPeng P(2023)A Hybrid Deep Learning Method to Extract Multi-features from Reviews and User–Item Relations for Rating PredictionInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00288-516:1Online publication date: 27-Jun-2023
https://doi.org/10.1007/s44196-023-00288-5
Gupta SSandhane R(2022)Use of sentiment analysis in social media campaign design and analysisCARDIOMETRY10.18137/cardiometry.2022.22.351363(351-363)Online publication date: 25-May-2022
https://doi.org/10.18137/cardiometry.2022.22.351363
Alengadan BKhan S(2018)Modified aspect/feature based opinion mining for a product ranking system2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)10.1109/ICCTAC.2018.8370393(1-5)Online publication date: Feb-2018
https://doi.org/10.1109/ICCTAC.2018.8370393
Alengadan BKhan S(2017)A proposed system for modifying aspect based opinion mining for ranking of products2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)10.1109/SSPS.2017.8071616(335-338)Online publication date: May-2017
https://doi.org/10.1109/SSPS.2017.8071616
Mahadevan AArock M(2017)Credible user-review incorporated collaborative filtering for video recommendation system2017 International Conference on Intelligent Sustainable Systems (ICISS)10.1109/ISS1.2017.8389433(375-379)Online publication date: Dec-2017
https://doi.org/10.1109/ISS1.2017.8389433

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