Understanding Consumer Perceptions About Smartwatches: Feature Extraction and Opinion Mining Using Supervised Learning Algorithm

Abstract

Against the backdrop of increasing smartwatch usage and the dynamic landscape of evolving features, a nuanced understanding of consumer opinions and preferences is vital for tailoring features and crafting effective marketing strategies. This study addresses this imperative by conducting a comprehensive analysis of customer reviews on smartwatches, aiming to determine the pivotal factors guiding consumer purchasing decisions. By employing word clouds to visually represent sentiments, the study uncovers notable trends. Positive reviews prominently highlight the term "quality", suggesting a strong emphasis on product excellence. In contrast, negative reviews were characterized by the prevalence of the term "fake", indicating concerns related to authenticity. Additionally, a comparative assessment of two machine learning algorithms, namely support vector machines and Naive Bayes, demonstrates that support vector machines exhibit superior accuracy in classification. These findings offer valuable insights for industry practitioners navigating the competitive landscape of the smartwatch market, providing actionable information for optimizing product features and refining marketing strategies to meet consumer expectations.

Keywords

• healthcare 5.0
• sentiment analysis
• opinion mining
• supervised learning algorithms
• wearable technology