학술논문

Topic Modeling to Extract Information from Nutraceutical Product Reviews
Document Type
Conference
Source
2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC) Consumer Communications & Networking Conference (CCNC), 2019 16th IEEE Annual. :1-6 Jan, 2019
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Data mining
Oils
Compounds
Business
Sentiment analysis
Tagging
Natural Language Processing
Topic Modeling
Machine Learning
Mobile Applications
Nutraceuticals
Language
ISSN
2331-9860
Abstract
Consumer purchases of Vitamins and other Nutraceuticals have grown over the past few years with most of the growth occurring in on-line purchases. However, general e- commerce platforms, such as Amazon, fail to cater to consumers’ specific needs when making such purchases. In this study, the authors design and develop a system to provide tailored information to consumers within this retail vertical. Specifically, the system uses Natural Language Processing (NLP) techniques to extract information from user-submitted nutraceutical product reviews. Using Natural Language Processing, three information streams are presented to consumers (1) a five point rating system for cost, efficacy and service, (2) a summary of topics commonly discussed about the product and, (3) representative reviews of the product. By presenting product-specific information in this manner we believe that consumers will make better product choices.