Online reviews are an important asset for users deciding to buy a product, see a movie, or go to a restaurant, as well as for managers making business decisions. The reviews in the e-commerce websites are usually accompanied by ratings, facilitating users to learn the reviews. However, a lot of reviews spread across forums or social media are written in a plain text, which do not have ratings, called non-rated review in this paper. From the perspective of sentiment analysis, this study develops a predictive framework to calculate the ratings for non-rated reviews. The idea behind the framework begins at a couple of observations: (1) the rating of the review depends on sentiment score of aspects as well as the number of positive and negative aspects in the review; (2) the sentiment score of an aspect is determined by its context. Viewing term-pairs co-occurring with aspects as their context, we conceive of a variant of Conditional Random Field model, called SentiCRF, for generating term-pairs and calculating their sentiment scores from a train set. Then we develop a cumulative logit model that uses aspects and their sentiments in a review to predict ratings of the review. In addition, we meet a challenge of class imbalance on calculating sentiment scores of term-pairs. We also propose a heuristic re-sampling method to tackle class imbalance. Experiments conducted on the YELP dataset demonstrate the predictive framework is feasible and effective on predicting rating of reviews.
Qiu, Jiangtao and Li, Yinghong, "LEVERAGING SENTIMENT ANALYSIS TO PREDICT RATINGS OF REVIEWS" (2016). PACIS 2016 Proceedings. 320.