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.
- 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.