2018 EMNLP EMNLP 2018

Is Nike female? Exploring the role of sound symbolism in predicting brand name gender

Abstract

AbstractAre brand names such as Nike female or male? Previous research suggests that the sound of a person’s first name is associated with the person’s gender, but no research has tried to use this knowledge to assess the gender of brand names. We present a simple computational approach that uses sound symbolism to address this open issue. Consistent with previous research, a model trained on various linguistic features of name endings predicts human gender with high accuracy. Applying this model to a data set of over a thousand commercially-traded brands in 17 product categories, our results reveal an overall bias toward male names, cutting across both male-oriented product categories as well as female-oriented categories. In addition, we find variation within categories, suggesting that firms might be seeking to imbue their brands with differentiating characteristics as part of their competitive strategy.

The Questioner
🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — sound symbolism
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio