Comparing Brand Perception Through Exploratory Sentiment Analysis in Social Media
The presented student project outlines a natural language processing pipeline for brand metric comparison in the Twitter ecosystem. Sentiment calculation for an unlabeled data set is demonstrated and calibrated using the statistical Central Limit Theorem as a guidance to anchor the sentiment indicator in a homogeneous market. The process is evaluated by comparing the sentimental market performance of three leading German logistics companies. A support for the value of sentiment analysis for automated customer feedback analysis in real time is concluded.
LWDA