Quantifying Customer Sentiments: A Machine Learning Approach to Analyzing Automobile Brand Perception on Twitter

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter. The research models the 'Brand Polarity Score', which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet's influence, characterized by the engagement metrics of the tweet and the author's follower count. We also demonstrate how this brand polarity score can effectively communicate near real-time brand positioning, providing a valuable tool for monitoring brand sentiment over time. The proposed Brand Polarity Score (BPS) not only gauges brand perception but also serves as a reliable tool for progressive and competitive analyses, contributing to a comprehensive understanding of brand dynamics. Qualitative and quantitative analyses are performed to validate the robustness of the proposed BPS system.