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Natural Language Processing for Studying Consumer Journey: A Case Study of Sneaker Shoppers

Jazmyn Singh

NIIT University, Neemrana, Rajasthan

98-101

Vol: 10, Issue: 2, 2020

Receiving Date: 2020-04-28 Acceptance Date:

2020-06-04

Publication Date:

2020-06-18

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http://doi.org/10.37648/ijrst.v10i02.009

Abstract

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

References

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  2. Song, Gil-Young, et al. 'Inter-category map: building cognition network of general customers through big data mining.' KSII Transactions on Internet and Information Systems, vol. 8, no. 2, Feb. 2014, pp. 134+. Gale AcademicOneFile, link.gale.com/apps/doc/A365070796/AONE?u=anon~1efd6a0e&sid=googleScholar&xid=5ad4868d. Accessed 15 Oct. 2023.
  3. N. Girija and S.K. Srivatsa, (2006). A Research Study: Using Data Mining in Knowledge Base Business Strategies. Information Technology Journal, 5: 590-600.
  4. Cambria E., White B., (2014); Jumping NLP Curves: A Review of Natural Language Processing Research; IEEE Computational Intelligence Magazine, 48-57.
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