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Customer Satisfaction Measurement with N-gram and Sentiment Analysis

Product reviews are an excellent source of information for qualified management decisions. Learn more about the right text mining techniques.

Petr Korab
TDS Archive
Published in
7 min readApr 10, 2023

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Photo by Freepik on Freepik

Introduction

Happy customers drive company growth. The five-word sentence explains everything about why we do our best to maximize customer satisfaction. Product reviews are one of the major data sources that large companies like Amazon and Apple, middle-sized exporters including Lentiamo, and local companies running their Facebook pages collect. Reviews are typically collected repeatedly over time, and factors like quality shifts, marketing communications, and customer care friendliness impact the sentiment expressed by customers.

Note: Image by author, based on the review of Karim (2011), Baker and Wurgler (2006), Merrin et al. (2013), and Eachempat et al. (2022)

The Business Intelligence (BI) role should be to analyze product reviews, identify potential problems, and develop hypotheses to solve them. In the next stage, these recommendations are scrutinized by other departments based on the company structure. This article will explain more closely the analytics of customer satisfaction measurement with

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Petr Korab
Petr Korab

Written by Petr Korab

Python engineer /NLP / data Viz. Text Mining Stories founder textminingstories.com

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