Online consumer reviews are important for the success of consumer products. To analyse star ratings, existing research has leveraged the expectancy-disconfirmation theory (EDT), but by deriving consumer expectations from prior star ratings rather than aspect-based sentiments in prior textual online reviews. The latter is promising since it can allow actionable insights for different product aspects such as service of restaurants. To operationalize an aspect-level research model, we leverage large language models in this work. Our results support, amongst others, that expectations, perceived performance and disconfirmation, each derived from textual reviews, have a unique explanatory power for star ratings with perceived performance having the highest one for all three considered product domains. This study contributes to a more thorough understanding of the explanatory power of textual online consumer reviews for star ratings and gives actionable insights for businesses to efficiently improve the average star rating as key performance indicator.