Central banks collecting information about customer satisfaction with the services provided by different bank. Also collects the information about the complaints.
- Bank users give ratings and write reviews about services on central bank websites. These reviews and ratings help to banks evaluate services provided and take necessary to action improve customer service. While ratings are useful to convey the overall experience, they do not convey the context which led a reviewer to that experience.
- If we look at only the rating, it is difficult to guess why the user rated the service as 4 stars. However, after reading the review, it is not difficult to identify that the review talks about good “service” and “expectations”.
So the Business Requirement is to analyze customer reviews and predict customer satisfaction with the reviews. It should include following tasks.
- Data processing
- Key positive words/negative words (most frequent words)
- Classification of reviews into positive, negative and neutral
- Identify key themes of problems (using clustering, topic models)
- Predicting star ratings using reviews
- Perform intent analysis
The data is a detailed dump of customer reviews/complaints (~500) of different services at different banks.
- Date (Day the review was posted)
- Stars (1–5 rating for the business)
- Text (Review text),
- Bank name
Let’s develop a machine learning model for further analysis.