APNEA: Intelligent Ad-Bidding Using Sentiment Analysis

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. CCS CONCEPTS • Information systems → Computational advertising; Content match advertising; Display advertising; Sentiment analysis.