Harnessing Sentiment Analytics: Insights into Customer Behavior and Decision-Making

Abstract

In the age of digital transformation, the ability to harness sentiment analysis offers significant insights into customer behavior and decision-making. This paper explores the use of Support Vector Machines (SVM) for sentiment classification and their application in analyzing customer feedback to provide businesses with valuable insights into customer preferences, purchasing decisions, and product satisfaction. The research demonstrates how sentiment analytics can be leveraged to understand the emotional drivers behind customer behavior, including how emotions such as excitement, frustration, or trust influence purchasing decisions, loyalty, and overall customer satisfaction. Using an extensive dataset of customer reviews from various platforms, we explore the effectiveness of machine learning techniques, particularly SVM, for classifying sentiments as positive, negative, or neutral. The findings highlight how businesses can adapt their marketing strategies, product offerings, and customer service practices by understanding the emotional patterns in customer feedback. This paper also proposes practical strategies for businesses to effectively incorporate sentiment analytics into their decision-making processes and offers recommendations for future research in improving the accuracy of sentiment analysis models, particularly in handling sarcasm, irony, and domain-specific language. By exploring the link between customer sentiment and behavior, this research provides insights that can guide businesses towards more personalized and responsive strategies.