A probabilistic rating inference framework for mining user preferences from reviews

We propose a novel Probabilistic Rating infErence Framework, known as PREF, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. PREF applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. PREF takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of PREF against several related algorithms, and suggest that PREF can produce reasonably good results using a small training corpus. We also describe a useful application of PREF as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated PREF with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.