Topic Modeling to Extract Information from Nutraceutical Product Reviews
| Autor: | Luba Gloukhova, Ker Yu Ong, Nicholas Ross, Kunal Kotian, Deena Liz John, Diane Myung-kyung Woodbridge, Ernest Kim, Tyler White |
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| Rok vydání: | 2019 |
| Předmět: |
Topic model
Service (business) Point (typography) Computer science 05 social sciences Sentiment analysis 02 engineering and technology Data science Nutraceutical Product reviews 0502 economics and business 0202 electrical engineering electronic engineering information engineering 050211 marketing 020201 artificial intelligence & image processing Product (category theory) |
| Zdroj: | CCNC |
| DOI: | 10.1109/ccnc.2019.8651723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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