DASA: Dissatisfaction-oriented Advertising based on Sentiment Analysis

  • Authors:

  • Guang Qiu;Xiaofei He;Feng Zhang;Yuan Shi;Jiajun Bu;Chun Chen

  • Affiliations:

  • Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China;College of Computer Science, Zhejiang University, Hangzhou 310027, China;Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China;Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China;Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China;Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 12.05

Visualization

Abstract

Online advertising has become one of the major revenue sources of today's Internet ecosystem. The main advertising channels used to distribute textual ads are sponsored search and contextual advertising. Here we consider the problem of contextual advertising, i.e. associating ads with a Web page. Most of previous work only focuses on topical relevance of ads whereas the consumer attitudes are ignored. In this paper, we propose a novel advertising strategy, called Dissatisfaction-oriented Advertising based on sentiment analysis (DASA), to simultaneously improve ad relevance and user experience. Specifically, by using syntactic parsing and sentiment dictionary, we propose a rule based approach to extract topic words of opinion sentences associated with negative sentiment, which are regarded as the advertising keywords. We also design a prototype system for product information submission for the sake of ad selection. We take into account the consumer attitudes and promote the competitors of those products with which the consumers are not satisfied. The experimental results on advertising keyword extraction and ad selection have demonstrated the effectiveness of the proposed approach.