HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Leveraging Wikipedia concept and category information to enhance contextual advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
An adaptive approach to chinese semantic advertising
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A generate-and-test method of detecting negative-sentiment sentences
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Electronic word of mouth analysis for service experience
Expert Systems with Applications: An International Journal
Audience targeting by B-to-B advertisement classification: A neural network approach
Expert Systems with Applications: An International Journal
Simulating the spread of opinions in online social networks when targeting opinion leaders
Information Systems and e-Business Management
Hi-index | 0.00 |
Web advertising (Online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the world’s most important marketing channels. This paper addresses the mechanism of Content-based advertising (Contextual advertising), which refers to the assignment of relevant ads to a generic web page, e.g., a blog post. As blogs become a platform for expressing personal opinion, they naturally contain various kinds of expressions, including both facts and comments of both a positive and negative nature. Besides, in line with the major tenet of Web 2.0 (i.e., user-centric), we believe that the web-site owners would be willing to be in charge of the ads which are positively related to their contents. Hence, in this paper, we propose the utilization of sentiment detection to improve Web-based contextual advertising. The proposed sentiment-oriented contextual advertising (SOCA) framework aims to combine contextual advertising matching with sentiment analysis to select ads that are related to the positive (and neutral) aspects of a blog and rank them according to their relevance. We experimentally validate our approach using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method can effectively identify those ads that are positively correlated with the given blog pages.