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

Objective:

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
 

Approach:

  • 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
 

Benefits:

  • 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.