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Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. However, while feature selection has been extensively studied for other classification tasks, is has not for ordinal regression. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal regression, and test them on two datasets of product review data.

Abstract. Online product reviews are becoming increasingly available, and are being used more and more frequently by consumers in order to choose among competing products. Tools that rank competing products in terms of the satisfaction of consumers that have purchased the product before, are thus also becoming popular. We tackle the problem of rating (i.e., attributing a numerical score of satisfaction to) consumer reviews based on their textual content. We here focus on multi-facet review rating, i.e., on the case in which the review of a product (e.g., a hotel) must be rated several times, according to several aspects of the product (for a hotel: cleanliness, centrality of location, etc.). We explore several aspects of the problem, with special emphasis on how to generate vectorial representations of the text by means of POS tagging, sentiment analysis, and feature selection for ordinal regression learning. We present the results of experiments conducted on a dataset of more than 15,000 reviews that we have crawled from a popular hotel review site.

Abstract-Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis / opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. However, while feature selection has been extensively studied for other classification tasks, is has not for ordinal classification. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal classification, and test them on two datasets of product review data against three metrics previously known from the literature, using two learning algorithms from the "support vector regression" tradition. The experimental results show that all four proposed metrics largely outperform all of the three baseline techniques, on both datasets and for both learning algorithms.

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