The paper presents a text analytics framework that analyses online reviews to explore how consumer-perceived negativity corresponding to the supply chain propagates over time and how it affects car sales. In particular, the framework integrates aspect-level sentiment analysis using SentiWordNet, time-series decomposition, and biascorrected least square dummy variable (LSDVc) - a panel data estimator. The framework facilitates the business community by providing a list of consumers' contemporary interests in the form of frequently discussed product attributes; quantifying consumer-perceived performance of supply chain (SC) partners and comparing the competitors; and a model assessing various firms' sales performance. The proposed framework demonstrated to the automobile supply chain using a review dataset received from a renowned car-portal in India. Our findings suggest that consumer-voiced negativity is maximum for dealers and minimum for manufacturing and assembly related features. Firm age, GDP, and review volume significantly influence car sales whereas the sentiments corresponding to SC partners do not. The proposed research framework can help the manufacturers in inspecting their SC partners; realising consumer-cited critical car sales influencers; and accurately predicting the sales, which in turn can help them in better production planning, supply chain management, marketing, and consumer relationships.