Online reviews are an important asset for users who are deciding to buy a product, see a movie, or go to a restaurant and for managers who are making business decisions. The reviews from e-commerce websites are usually attached to ratings, which facilitates learning from the reviews by users. However, many reviews that spread across forums or social media are written in plain text, which is not rated, and these reviews are called non-rated reviews in this paper. From the perspective of sentiment analysis at the aspects level, this study develops a predictive framework for calculating ratings for non-rated reviews. The idea behind the framework began with an observation: the sentiment of an aspect is determined by its context; the rating of the review depends on the sentiment of the aspects and the number of positive and negative aspects in the review. Viewing term pairs that co-occur with aspects as their context, we conceived of a variant of a Conditional Random Field model, called SentiCRF, for generating term pairs and calculating their sentiment scores from a training set. Then, we developed a cumulative logit model that uses aspects and their sentiments in a review to predict the ratings of the review. In addition, we met the challenge of class imbalance when calculating the sentiment scores of term pairs. We also conceived of a heuristic re-sampling algorithm to tackle class imbalance. Experiments were conducted on the Yelp dataset, and their results demonstrate that the predictive framework is feasible and effective at predicting the ratings of reviews. Published by Elsevier Inc.