text: We present opinion recommendation, a novel task of jointly predicting a custom review with a rating score that a certain user would give to a certain product or service, given existing reviews and rating scores to the product or service by other users, and the reviews that the user has given to other products and services. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning, which is the strength of neural models. We use a single neural network to model users and products, capturing their correlation and generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on this http URL data compared with Yelp's own ratings, and our methods give better results compared to several pipelines baselines using state-of-the-art sentiment rating and summarization systems.
  @cite_28: Aspect-based opinion mining from online reviews has attracted a lot of attention recently. Given a set of reviews, the main task of aspect-based opinion mining is to extract major aspects of the items and to infer the latent aspect ratings from each review. However, users may have different preferences which might lead to different opinions on the same aspect of an item. Even if fine-grained aspect rating analysis is provided for each review, it is still difficult for a user to judge whether a specific aspect of an item meets his own expectation. In this paper, we study the problem of estimating personalized sentiment polarities on different aspects of the items. We propose a unified probabilistic model called Factorized Latent Aspect ModEl (FLAME), which combines the advantages of collaborative filtering and aspect based opinion mining. FLAME learns users' personalized preferences on different aspects from their past reviews, and predicts users' aspect ratings on new items by collective intelligence. Experiments on two online review datasets show that FLAME outperforms state-of-the-art methods on the tasks of aspect identification and aspect rating prediction.
  @cite_22: We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star". We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.
  @cite_12: 
summary: There has been work integrating sentiment analysis and recommendation systems, which use recommendation strategies such as matrix factorization to improve the performance of sentiment analysis @cite_22 @cite_12 . These methods typically use ensemble learning @cite_12 or probabilistic graph models @cite_28 . For example, who proposed a factor graph model to recommend opinion rating scores by using explicit product features as hidden variables.