Applying natural language processing algorithm for predicting consumer product preferences in retail stores

Abstract

Technology has changed the way retailers predict and understand consumer behaviour. One such technology that can enable retailers to understand consumer preference is Natural Language Processing (NLP). Social media content including the opinions and interests of the customers is recognized as a valuable source of information for businesses. This study aims to perform a semantic analysis of tweets with the use of an NLP algorithm. This study focuses on building an intelligent application capable of predicting the category of goods a customer would most likely buy in a retail store. This study focuses on analysing social media data with NLP to predict what a customer would buy in a retail store. In this study, we measured a 0.3 increase in accuracy when only various forms of nouns were extracted and analysed. Further research may include Named-Entity Recognition (NER), especially for proper nouns. The researchers believe that this study will contribute to changing the trajectory in which NLP is applied in the retail industry. Therefore, the methodology and design used herein will improve the existing approaches that have already been employed concerning NLP and social media data analysis.