Social Media Text Analytics

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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