research-article

APNEA: Intelligent Ad-Bidding Using Sentiment Analysis

Authors:

Samuel Suraj Bushi,

Osmar ZaianeAuthors Info & Claims

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

Pages 76 - 83

https://doi.org/10.1145/3350546.3352503

Published: 14 October 2019 Publication History

Abstract

Online advertising is one of the most lucrative forms of advertising, making it an important channel of advertising media. Contextual Advertising is a type of online display advertising that takes cues from the content of the triggering page and displays advertisements that are relevant to the current context. However, on several occasions, the context may have a negative connotation, and displaying advertisements that are relevant to it might prove to be detrimental to the advertiser. We refer to such a scenario as an unfortunate placement. In this work, we propose APNEA (Ad Positive NEgative Analysis), a light-weight system that uses a sentiment-oriented approach to rank the advertisers such that positively correlated brands are ranked higher than brands that are neutral or negatively correlated. Experiments show that APNEA helps avoid unfortunate placements while maintaining ad-relevance. It outperforms several baselines in terms of accuracy on human-annotated test data while having a lower run-time, which is crucial for real-time bidding systems.

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Leung CJiang FZhang Y(2020)Explainable Machine Learning and Mining of Influential Patterns from Sparse Web2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00128(829-836)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00128

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APNEA: Intelligent Ad-Bidding Using Sentiment Analysis

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cover image ACM Other conferences

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 2019

507 pages

ISBN:9781450369343

DOI:10.1145/3350546

Copyright © 2019 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 the author(s) 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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Publication History

Published: 14 October 2019

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WI '19

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

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Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Leung CJiang FZhang Y(2020)Explainable Machine Learning and Mining of Influential Patterns from Sparse Web2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00128(829-836)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00128

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