The global transition toward electric mobility is crucial in mitigating climate change, reducing air pollution, and promoting sustainable urban transportation. India, one of the fastest-growing markets for electric vehicles (EVs), has witnessed a surge in electric two-wheeler (E2W) adoption. However, concerns regarding battery longevity, charging infrastructure, and affordability remain key barriers to widespread adoption. This study applies sentiment analysis techniques to assess consumer perceptions of electric bikes using machine learning models for sentiment classification. A dataset comprising 3,395 consumer reviews was collected from leading automotive platforms, including BikeWale, BikeDekho, OneDrive, and ZigWheels, using web scraping techniques. The data was analyzed using VADER, TextBlob, Naïve Bayes, Logistic Regression, and Support Vector Machines (SVM) to classify sentiment and identify key consumer concerns. The results indicate a predominantly positive sentiment towards electric bikes, driven by environmental benefits and cost savings. However, consumers expressed concerns over battery efficiency, charging station availability, and high initial costs. Among the models tested, SVM achieved the highest accuracy, making it the most effective in sentiment classification. This study contributes to the limited academic research on electric bikes, offering data-driven insights into consumer perceptions. By utilizing real-world consumer data from widely used automotive platforms, the research provides valuable information for policymakers, manufacturers, and industry stakeholders. The findings aim to assist in developing strategies to address consumer concerns and enhance electric bike adoption in India.