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Topic Classification and Sentiment Analysis for Generating Social Sentiment Score for Airlines


How to analyse comments from various sources on airlines (and their competitors) to track sentiments associated with them.


Key Challenges:

  • Presence of various sources of unstructured data:
    • Customer Reviews
    • Social Media Posts
  • Lack of a comprehensive scoring method, combining all the above sources of information


  • Corpus of vectors (words) made from raw and unstructured data
  • Pre-processing engine for white-space removal, punctuation-removal, stop-words removal, etc.
  • Term document matrix creation
  • Text Classification and NLP Algorithms to categorize each comment into classes
  • Sentiment Classifier engine using an ensemble of algorithms to arrive at a weighted sentiment score with optimal weights to reduce errors


  • Provided comprehensive customer sentiment score to airline
  • Enabled the airline to identify avenues that customers are consistently dissatisfied with
  • Provided sentiment scores across time for airlines to correlate the impact of various marketing, promotional and brand-awareness campaigns on the customer sentiments.