## 1 Introduction  
Due to the development of Web 2.0 and the popularity of mobile devices, everyone is free to share their opinions and publish articles on the Internet. As a result, the amount of information on the Internet grows exponentially with time; as a further result, users need to spend a lot of time to find the right data that meet their needs; thus, the efficiency of decision making is reduced. In addition, compiling and analyzing a huge amount of data has become a major problem. To solve the above-mentioned problems, many scholars have proposed recommendation methods or systems to filter unnecessary information. Recommender systems mainly analyze users' preferences, behaviors, and community relations, prior to recommending items that may be of interest in future searches [1, 2, 3, 4]. Traditional recommendation methods analyze user preferences based on ratings; however, ratings cannot always accurately represent users' preferences. Many e-commerce and social networking sites, such as Amazon.com and Yelp.com, allow users to comment on products, give ratings, or engage in discussions. The text review data may contain a lot of potentially important information. Different aspects, sentiment, and semantics hidden in the reviews can express user preferences and product features. As the review data are unstructured, it is difficult to consider and acquire lexical or grammatical issues for analysis. For analyzing text reviews, some scholars proposed methods such as topic models [5, 6] and machine learning methods [7, 8] to automatically extract the contexts of articles or identify valuable knowledge. These studies focused on determining how to generate the above-mentioned aspects, their polarities, or sentiments from user reviews. However, these methods only consider these three factors simultaneously when extracting the aspects from the review texts. Additionally, the semantics and sentiments which are implicit in text reviews can reveal users' preferences. For extracting the semantics from the texts, deep learning methods can be used to analyze users and reviews on products; then, collaborative filtering can be used to predict ratings [9]. [PERSON] et al. [10] used deep cooperative neural networks to predict ratings based on user behavior and product reviews. To understand the expression of sentiments in the text, analyzing the emotion-related vocabulary contained in texts can provide insights into the sentiments and attitudes conveyed by users in their texts [11]. On the other hand, sentiment polarity or intensity can be used to represent the users' emotional performance [12, 13]. These studies show that deriving semantics or sentiments from reviews or text can capture user preferences and perceived product qualities. Using deep learning or neural network methods can improve the performance and accuracy of both prediction and classification. However, these studies only analyzed the semantics or sentiments in the reviews, without considering user-preferred aspects implied in user reviews. To improve the accuracy of recommendation methods, determining how to extract accurate user preferences and features based on user behavior is an important issue. Besides users' rating data, developing the methods for identifying user-preference aspects, semantic and sentiment features hidden in text reviews, and combining the implicit and explicit information in a recommendation method help to generate insights into user preferences and product features. To address the problem in the existing studies, this work proposes an aspect-based deep learning rating prediction method (ADLRP) for review websites, which consists of four main components: aspect detection, sentiment analysis, semantic analysis, and rating prediction. In the aspect of detection, sentence-level latent Dirichlet allocation (SLDA) [6, 14] was used to analyze the topics at the sentence level, in order to identify the implicit aspect of the reviews. The word embedding algorithm was then used to generate aspect embedding vectors.