Brand Celebrity Matching Model Based on Natural Language Processing

TL;DR

This paper introduces a novel NLP-based model for matching brands with celebrities for endorsement, demonstrating superior performance and pioneering the use of NLP in this application.

Contribution

The paper presents the first NLP-based model for brand-celebrity matching, offering a new methodology for endorsement suitability assessment.

Findings

Achieved a 0.362 F1 score in matching accuracy

Outperformed existing baseline models

Validated effectiveness in real-world scenarios

Abstract

Celebrity Endorsement is one of the most significant strategies in brand communication. Nowadays, more and more companies try to build a vivid characteristic for themselves. Therefore, their brand identity communications should accord with some characteristics as humans and regulations. However, the previous works mostly stop by assumptions, instead of proposing a specific way to perform matching between brands and celebrities. In this paper, we propose a brand celebrity matching model (BCM) based on Natural Language Processing (NLP) techniques. Given a brand and a celebrity, we firstly obtain some descriptive documents of them from the Internet, then summarize these documents, and finally calculate a matching degree between the brand and the celebrity to determine whether they are matched. According to the experimental result, our proposed model outperforms the best baselines with a 0.362 F1 score and 6.3% of accuracy, which indicates the effectiveness and application value of our model in the real-world scene. What's more, to our best knowledge, the proposed BCM model is the first work on using NLP to solve endorsement issues, so it can provide some novel research ideas and methodologies for the following works.