Unleashing Customer Insights: Harnessing Machine Learning Approaches for Sentiment Analyzing and Leveraging Customer Feedback

Unleashing Customer Insights: Harnessing Machine Learning Approaches for Sentiment Analyzing and Leveraging Customer Feedback

Debosree Ghosh
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-2647-3.ch012
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Abstract

This chapter explores the integration of machine learning with customer sentiment analysis to unveil insights from customer feedback. It emphasizes the importance of understanding customer sentiment for enhancing satisfaction and making informed decisions. The chapter covers various machine learning approaches including supervised and unsupervised learning, as well as deep learning models. Preprocessing techniques and feature engineering methods for textual data are discussed. The challenges of sentiment analysis, such as sarcasm and context, are addressed, along with practical applications in product development, brand management, and personalized marketing. Ethical considerations are highlighted. Overall, this chapter provides valuable insights on leveraging machine learning for customer sentiment analysis.
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Introduction

In today's digital age, customers are more vocal than ever before. They are constantly sharing their opinions, experiences, and feedback on social media, review sites, and other online platforms. This wealth of data provides businesses with a unique opportunity to understand customer sentiment and gain insights into how they can improve their products and services. This chapter explores the intersection of machine learning and customer sentiment analysis, focusing on how machine learning approaches can be harnessed to analyze and leverage customer feedback effectively.

Literature on Sentiment Analysis

The research on sentiment analysis is extensive and growing rapidly.

Turney (2002): Turney proposed a simple approach to sentiment analysis using point wise mutual information (PMI). PMI is a measure of the association between two words, and Turney used it to identify the sentiment of words by their association with known positive and negative words.

Pang and Lee (2002): Pang and Lee proposed a supervised approach to sentiment analysis using machine learning. They trained a classifier to predict the sentiment of sentences by using a set of labelled examples.

Liu et al. (2005): Liu et al. proposed a feature-based approach to sentiment analysis. They identified a set of features that are useful for predicting sentiment, such as the presence of certain words or phrases, and used these features to train a classifier.

Since these early works, there have been many advances in the field of sentiment analysis. New ML algorithms have been developed, and researchers have explored new ways to leverage customer feedback data for sentiment analysis.

Literature on Sentiment Analysis for Customer Feedback

There is a growing body of research on using sentiment analysis to analyse customer feedback.

Hu and Liu (2004): Hu and Liu proposed a method for using sentiment analysis to identify customer opinion leaders. They used a graph-based approach to identify customers who are highly influential in social networks.

Wang et al. (2011): Wang et al. proposed a method for using sentiment analysis to identify product defects. They used sentiment analysis to identify customer reviews that express negative sentiment about specific product features.

Asur and Huberman (2010): Asur and Huberman proposed a method for using sentiment analysis to predict stock market movements. They used sentiment analysis to identify positive and negative sentiment about companies in social media data, and found that this sentiment could be used to predict stock prices.

These studies demonstrate the potential of sentiment analysis for analysing customer feedback. Sentiment analysis can be used to identify customer opinion leaders, identify product defects, predict customer churn, and more.

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