2008 LearningfromMultiTopicWebDocume

Subject Headings:

Notes

It suggests that focusing on relevant topics written with positive sentiment produces high click-through rates.
It is related to: (Fan & Chang, 2009) ⇒ Teng-Kai Fan, and Chia-Hui Chang. (2009). “Sentiment-Oriented Contextual Advertising.” In: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval (ECIR 2009). doi:10.1007/978-3-642-00958-7_20

sub-document classification, contextual advertising, sensitive content detection, opinion mining

Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges. Often advertisers wish to either target (or avoid) some specific content on web pages which may appear only in a small part of the page. Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training. In this paper we investigate ways to learn for sub-document classification when only page level labels are available - these labels only indicate if the relevant content exists in the given page or not. We propose the application of multiple-instance learning to this task to improve the effectiveness of traditional methods. We apply sub-document classification to two different problems in contextual advertising. One is “sensitive content detection” where the advertiser wants to avoid content relating to war, violence, pornography, etc. even if they occur only in a small part of a page. The second problem involves opinion mining from review sites - the advertiser wants to detect and avoid negative opinion about their product when positive, negative and neutral sentiments co-exist on a page. In both these scenarios we present experimental results to show that our proposed system is able to get good block level labeling for free and improve the performance of traditional learning methods.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 LearningfromMultiTopicWebDocume	John C. Platt Arun C. Surendran Yi Zhang Mukund Narasimhan			Learning from Multi-topic Web Documents for Contextual Advertisement		KDD-2008 Proceedings	http://research.microsoft.com/en-us/um/people/acsuren/kdd593-zhang.pdf	10.1145/1401890.1402015		2008