A Text Mining Approach for Analyzing Consumer Sentiment and Behavior

This paper examines the application of text analysis methods to evaluate customer behavior and preferences using The Amazon Consumer Behavior Dataset. The study aims to uncover meaningful insights into customer interactions with Amazon's platform, focusing on four key methods: sentiment analysis, word frequency, topic modeling, and clustering. These methods are employed to analyze textual responses within the dataset, enabling a deeper understanding of customer sentiment, frequently mentioned themes, underlying topics, and potential customer groupings. By combining these analytical techniques, the research identifies patterns in user behavior and preferences that can inform Amazon's decision-making processes. The findings highlight key drivers of customer satisfaction and areas for improvement, such as service appreciation and recommended changes to product quality. The paper emphasizes the value of text analysis in uncovering actionable insights and concludes with recommendations for Amazon to enhance its services, aligning strategies with customer needs to maintain its leadership in the e-commerce industry.