The growth of online shopping has made online reviews a critical source of information for consumers. There are, however, lots of them, in the thousands and sometimes hundreds of thousands for a single product. And these reviews keep arriving persistently over time, be it the ones that rate the product highly or the ones that rate it less favorably. With the information in those reviews publicly available online, the post-purchase experiences of millions of customers are literally put on display. Through a deep dive into the content of reviews over time and across satisfaction levels, this paper studies the text of online reviews through the lens of information revelation. Using a novel methodology, we deconstruct reviews into the aspects they discuss, the importance they associate with those aspects and the satisfaction they express towards them. We then apply this methodology to a large review dataset from Amazon. This allows us to evaluate the temporal evolution of user satisfaction with these aspects at a granular level. We find that aspects being discussed do not change over the half-life of products. We also find that user satisfaction with these aspects are very different when comparing favorable reviews to less favorable ones. Somewhat surprisingly, aspects that generate a strong positive satisfaction for positive reviews have a neutral or muted mention in negative reviews. Our work has major implications to a variety of stakeholders; the platform hosting the reviews, the sellers and the customers.