# Review Rating Prediction Based on User Context and Product Context
## 1 Introduction  
The rapid development of Web 2.0 and e-commerce has led to a proliferation in the number of online user reviews. Online reviews contain a wealth of sentiment information that is important for many decision-making processes, such as personal consumption decisions, commodity quality monitoring, and social opinion mining. Mining the sentiment and opinions that are contained in online reviews has become an important topic in natural language processing, machine learning, and Web mining. The sentiment classification of online reviews is the most fundamental and important work in natural language processing [1; 2]. At present, the review sentiment classification (positive and negative) has been widely studied, but the review sentiment classification cannot meet the demand for fine-grained review sentiment analysis [3; 4; 5; 6; 7]. For example, how do consumers choose the most appropriate product from several products that are being reviewed? [PERSON]'s research shows that consumers are willing to pay an extra 20% to 99% to buy a five-star rated product instead of a four-star rated one [8]. This shows that the nuances of product ratings can lead to dramatic changesin product sales. In public opinion monitoring, the government not only wants to understand the positive and negative sentiment categories of the reviews, but also wants to further understand the positive and negative sentiment intensity to distinguish the urgency of public opinion events and take different measures. As a result, researchers are paying more and more attention to review rating predictions (RRP). Existing RRP methods are mainly based on the review content. From the perspective of natural language processing, the review content is converted into a text feature vector, and then a linear regression model is used to model the RRP [9; 10; 11; 12; 13]. For example, [PERSON] et al. regarded the RRP as a feature engineering problem, and improved the performance of RRP by extracting different features, such as words, lexical patterns, syntactic structures, and semantic topics from the review content [10]. [PERSON] et al. used deep CNN on word embedding to learn the features of reviews, which are then fed into a softmax layer to obtain sentiment predictions [13]. Existing RRP methods that are based on review content have an implicit hypothesis: the sentiment polarity and intensity expressed by the same sentiment words is consistent, and the sentiment polarity and intensity expressed by different sentiment words is different. However, this implicit assumption does not hold in the actual scenario. For example, a demanding user gave a five-point rating to the Huawei mobile phone with the sentiment word \"good\", while another tolerant user might give a four-star rating with the same sentiment word \"good\". Different users use same sentiment words to express different sentiment intensity, which reflects the user context of sentiment word is very important to RRP. For another example, the sentiment word \"simple\" in the review \"this movie is simple\" expresses negative sentiment. While in the review \"this phone is simple to use\", it expresses positive sentiment. For different products, the same sentiment words are used to express different sentiment polarities, which reflect the product context of sentiment word has a certain impact on RRP. Through the above analysis, it can be found that RRP is not only related to the review text content, but is also related to the user context information and the product context information. Some studies have shown that user context information is an important data source for online review rating prediction [14; 15; 16; 17]. [PERSON] et al. believe that the rating is not entirely determined by the review content, because a demanding user may comment on all products with harsh words, even if he gives a very high rating to the product [14]. Some researchers believe that product context information is another important data source for online review rating prediction. [PERSON] et al.