Beyond the Scent: A Holistic NLP Study of the Fragrance World

This paper presents a holistic NLP-based approach to the fragrance industry. Using a methodology based on online reviews, this study aimed to gain insights into customer sentiment and product preferences towards fragrance brands. In this study, analyses were conducted by creating a unique dataset by randomly selecting 13,828 reviews of 36 perfumes from a website specializing in the global perfume industry. By sentiment analysis, it is investigated how well user comments’ sentiment compound scores and overall perfume evaluations correlate. Using Latent Dirichlet Allocation method, commonly used terms and topics are extracted from comments to provide insights into user sentiments. Moreover, Term Frequency-Inverse Document Frequency analysis helped to draw out keywords and word patterns from evaluations that are distinctive to a given brand. The most commonly used words in these comments were identified by examining user reviews. By bridging perfumes and language within user-generated content, we believe this study would contribute valuable insights to management practices in the fragrance industry.