Jazmyn Singh
NIIT University, Neemrana, Rajasthan
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http://doi.org/10.37648/ijrst.v10i02.009
This research paper explores the use of natural language processing (NLP) and data mining to study the consumer journey of sneaker shoppers. We scraped tweets from Twitter and other social media platforms to collect data on sneaker shoppers' preferences, opinions, and experiences. We then used NLP techniques to clean and process the data, and to extract sentiment information from the tweets. Finally, we developed a product scoring system to identify the sneaker products that were most popular with consumers and had the highest customer satisfaction ratings.
Keywords: NLP; data mining; sneaker shoppers; sentiment analysis; product scoring; social media; customer pain points; product recommendations; targeted advertising; machine learning
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