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Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches by Md Shamim Hossain; Mst Farjana Rahman is a Business, Management and Accounting article available to read on EtoBox.

What is Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches about?

The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages

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Author

Md Shamim Hossain; Mst Farjana Rahman

Publisher

SAGE Publications

Published

2023

Language

EN

Field

Business, Management and Accounting (Social Sciences)