Text Analytics And NLP Models To Detect Industry Trends From Social Media

Case Study

Text Analytics and NLP Models
to Detect Industry Trends from Social Media

Business Objective

Our client is a European market insights firm in the Food & Beverage industry.

Their core offering is a one-of-a-kind platform which tracks global product launches and provides insights on product launches and their attributes such as ingredients, flavors, etc. They had two objectives – detect popular themes from product descriptions of packaged F&B products, and corroborate this using stated preferences on social media.

Challenges

  • Posts on social media, especially tweets, tend to be short and normal rules of grammar often do not apply
  • Eliminating redundancy by identifying and eliminating synonyms in addition to standard text parsing and data cleansing
  • 2+ million unique products across 125+ F&B categories, 10+ years, and 75+countries.

Solution Methodology

  • Processed text of the product labels and claims. The activity included parsing, cleansing, lemmatizing, and POS tagging
  • Identified and acquired relevant tweets through the Twitter API. Parsed and cleansed the text to prepare it for modeling
  • Identified topics – stable and transient – using variants of Latent Dirichlet Allocation models
  • Used Bayesian classifiers to tag new tweets into existing topics, if appropriate
  • Used fuzzy matching to map topics between processed documents and key F&Bthemes identified from tweets
  • Developed an insights dashboard showing key trends for each F&B category, by geography.

Business Impact

  • An automated platform that eliminates the need for manual research and subjective interpretation while detecting popular themes in a category.
  • Ability to quickly generate customized, ready-to-consume insights for any audience.
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