Sentiment analysis aims to judge the emotional tendency of the text, while the review quality prediction aims at judging the quality of the review. Sentiment analysis and review quality detection are two key tasks in sentiment analysis, these two tasks are closely related by many factors, the reviews on the same product have similar opinion polarity, and the quality of reviews from the same user tend to be similar. Therefore, this study proposes a joint neural model to learn sentiment analysis and quality prediction in order to better study the correlation between sentiment classification and review quality prediction tasks and the impact of user information and product information on sentiment classification and review quality prediction respectively. First of all, this study employs a deep representation learning approach to learn textual information of reviews, serving as the basis to connect two tasks, and then uses the user reviews and product reviews as the representation of the user and the representation of the product, on the basis, a user attention is adopted to encode user information in user representation, and a product attention is used to encode product information in product representation, and finally both user and product representations are jointly integrated for sentiment analysis and quality prediction with attention mechanism. The experimental results on the Yelp2013 and Yelp2015 datasets show that the proposed model can effectively improve the performance of sentiment analysis and online review quality prediction compared with the existing neural network models.