Sentiment-Oriented Contextual Advertising

Fan, Teng-Kai; Chang, Chia‐Hui

doi:10.1007/978-3-642-00958-7_20

Cited by 24 publications

(20 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a sentiment classifier might classify a user review about a movie as positive or negative depending on the sentiment expressed in the review. Sentiment classification has been applied in numerous tasks such as opinion mining [3], opinion summarization [4], contextual advertising [5], and market analysis [6]. For example, in an opinion summarization system it is useful to first classify all reviews into positive or negative sentiments and then create a summary for each sentiment type for a particular product.…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Bollegala

Weir

Carroll

2013

IEEE Trans. Knowl. Data Eng.

227

150

View full text Add to dashboard Cite

(2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25 (8). pp. 1719 -1731 . ISSN 1041 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/43452/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract-Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. Typically, sentiment classification has been modeled as the problem of training a binary classifier using reviews annotated for positive or negative sentiment. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance because words that occur in the train (source) domain might not appear in the test (target) domain. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised...

show abstract

Section: Introductionmentioning

confidence: 99%

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Bollegala

Weir

Carroll

2013

IEEE Trans. Knowl. Data Eng.

227

150

View full text Add to dashboard Cite

show abstract

“…As shown in previous studies, strong relevance increases the number of click-throughs [1] [3] [10] [13]. Some studies [6] [14] have also demonstrated that focusing on relevant topics written with positive sentiment produces high click-through rates. Although a page-relevant topic is a way to capture visitors' interest, there is no other way to determine their personal interests.…”

Section: Introductionmentioning

confidence: 79%

Blogger-centric contextual advertising

Fan

Chang

2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

Self Cite

View full text Add to dashboard Cite

This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers' interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers' immediate personal interests in order to improve online contextual advertising. The proposed BCCA (Blogger-Centric Contextual Advertising) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger's personal interests.

show abstract

“…8) Sentiment detection. [56] propose the utilization of sentiment detection to select ads that are related to the positive (and neutral) aspects of blog posts. 9) Ontology matching.…”

Section: Analytical Challengesmentioning

confidence: 99%

An overview of computational challenges in online advertising

Chatwin¹

2013

2013 American Control Conference

View full text Add to dashboard Cite

Online advertising is a large and rapidly growing business. The major players in the space, namely advertisers, publishers, and ad exchanges, are developing increasingly sophisticated systems, methods and tools to facilitate, manage, optimize and report on the performance of online advertising marketplaces and campaigns. Developing solutions that are both mathematically sound and practical draws on techniques from a variety of disciplines including machine learning, stochastic optimal control, information retrieval, data mining, natural language processing, and econometrics. In this paper, we provide an overview of the online advertising space, and identify, frame, and describe solution approaches to some of the major computational challenges in the space. We describe specific examples from industry applications, including ad inventory auctions, bidding and allocation strategies for ad inventory, inventory targeting, banner and landing page optimization, and performance estimation.

show abstract

Sentiment-Oriented Contextual Advertising

Cited by 24 publications

References 8 publications

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Blogger-centric contextual advertising

An overview of computational challenges in online advertising

Contact Info

Product

Resources

About