Abstract

The customer feedback management system that allows business institutions to manage their user suggestions and complaints. Feedback analytics use customer feedback data to measure customer experience and improve customer satisfaction. Feedback data is collected using key indicators and feedback metrics, which turned into actionable information. The main objective of this project is to develop a GUI for manipulation of feedback records in KEWAUNEE SCIENTIFIC CORPORATION, Bangalore. Here manipulation of records includes Creating, Searching, Updating, Viewing and deleting the records. Feedback records includes Date of feedback provided, Client Company name, Client name, Location, Source, Summary, Agency recommendations, Pre purchase experience, Various ratings, Overall Experience, Competitor experience, Timelines, Kewaunee resources, Strengths, Areas of improvement, Issues, Maintenance of product, Suggestions, Engagement, Future plan, Background, Customer opinion, Design, Immediate actions, Services, Insurance, etc. Visualization of records is done to validate the organisational improvements. Sentimental analysis is done to calculate the sentiment score of the positive and negative records. Text classification is performed using Naive Bayes algorithm and SVM algorithm. Classifier model is been built and accuracy is been computed efficiently. It is observed that Naive Bayes algorithm provides a better accuracy than SVM algorithm

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