PriyabrataThatoi/Complaint-Classifier

Customer complaints are often misclassifier due to lack of business knowledge. This leads to wastage of time and often larger wait time for customers. To resolve this NLP based LDA & TF-IDF can be used to correctly classify.

COMPLAINT CLASSIFIER USING NLP (LDA & TF-IDF)

Problem: While filing a complaint, customers are asked to choose the complaint category or theme. However, customers are unaware of business terms so they often choose the wrong category. This was a major issue for us with more than 20% of complaints being misclassified and routed incorrectly. This led to long wait times and incomplete resolution of customer complaints.

Solution: We leveraged topic algorithms such as TF-IDF and LDA to reclassify customer complaints based on the exact language used in the complaint. As seen in the chart below, there are several differences between original (customer-led) and new (NLP-recommended) themes. Especially, NLP-themes identified ‘Late Fees’ as the main reason (17%) for customer complaints, which was considerably higher than the previous estimate.

• Kaggle: https://www.kaggle.com/dhorvay/consumer-complaint-classifier-lda-topic-modeling
• Reference : https://towardsdatascience.com/https-medium-com-vishalmorde-humanizing-customer-complaints-using-nlp-algorithms-64a820cef373