Audience targeting by B-to-B advertisement classification: a neural network approach

From Brede Wiki
Jump to: navigation, search
Paper (help)
Audience targeting by B-to-B advertisement classification: a neural network approach
Authors: Alan Samuel Abrahams, Eloise Coupey, Eva X. Zhong, Reza Barkhi, Pete S. Manasantivongs
Citation: Expert Systems with Applications 40 (8): 2777-2791. 2013 June
Database(s): Google Scholar cites
DOI: http://dx.doi.org/10.1016/j.eswa.2012.10.068.
Link(s): http://www.sciencedirect.com/science/article/pii/S095741741201192X?v=s5#
Search
Web: Bing Google Yahoo!Google PDF
Article: BASE Google Scholar PubMed
Restricted: DTU Digital Library
Other: NIF
Services
Format: BibTeX
Extract: Talairach coordinates from linked PDF: CSV-formated wiki-formated

Audience targeting by B-to-B advertisement classification: a neural network approach describes automated text classification in advertissement classification.

Contents

[edit] Method

[edit] Data

The data was captured by 44 undergraduate business major students and contained:

  • Advertisement data from Business-to-business magazines
    • Entrepeneur
    • Inc.
  • 5,288 advertisements from July 2007-June 2011

The Data was divided into 7 classes: Capital access, compliance, computer technology, customer service, marketing & innovation, "luxury, travel & personal" and workforce.

[edit] Results

Among their results was that a high valance was associated with it being more probably that the advertisement "Marketing & Innovation" group compared to the computer technology group. They believe that it is due to that the Marketing & Innovation group use more possitive words.

[edit] Critique

  1. The AFINN word list is sometimes misspelt as AFFIN and the reference is from 2011, not 2012 as stated in the text.
  2. "The AFFIN dictionary is a major revision of the ANEW dictionary". This is not quite right. The AFINN word list is an independent development and not suppose to be a "revision" of ANEW.
Personal tools