论文标题
基于自然语言处理的品牌名人匹配模型
Brand Celebrity Matching Model Based on Natural Language Processing
论文作者
论文摘要
名人认可是品牌交流中最重要的策略之一。如今，越来越多的公司试图为自己建立生动的特征。因此，他们的品牌身份交流应符合人类和法规的某些特征。但是，以前的作品主要是通过假设停止的，而不是提出一种特定的品牌和名人之间匹配的方式。在本文中，我们提出了基于自然语言处理（NLP）技术的品牌名人匹配模型（BCM）。鉴于品牌和名人，我们首先从互联网上获得了一些描述性文档，然后总结了这些文档，最后计算品牌和名人之间的匹配程度，以确定它们是否匹配。根据实验结果，我们提出的模型以0.362 F1得分和精度的6.3％优于最佳基线，这表明我们模型在现实世界中的有效性和应用值。更重要的是，据我们所知，拟议的BCM模型是使用NLP解决认可问题的第一项工作，因此它可以为以下工作提供一些新颖的研究思想和方法。
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.
