# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
## 2 Literature Review
### Deep Learning-Based Sentiment Analysis  
Mining sentiments from natural language is challenging because it requires a deep understanding of the explicit and implicit, regular and irregular, and syntactical and semantic language rules [22]. Consequently, deep learning-based sentiment analysis has lately received a lot of attention and can be divided into two steps: the first is text preprocessing which was demonstrated to directly influence the classification accuracy, and the second is the sentiment classifier which is the main core of sentiment analysis [23; 24].  
Related to text preprocessing works, the authors of [25] presented the design of a Convolutional Neural Network built on top of a Word2 vec to allow for the use of task-specific and static vectors. His results add to well-established evidence the use of unsupervised pre-trained word vectors is an important ingredient for Natural Language Preprocessing. Similarly, the authors of [26] proposed a new model called SO-SD to build a classifier of Weibo messages using Word2 vec. Likewise, the authors of [27] proposed a deep Convolutional Neural Network that easily exploits the richness of a word embedding to perform a comment's sentiment analysis. They showed that using only a word embedding as input features is enough to achieve state-of-the-art results.  
Regarding the last advances in sentiment classifier algorithms, the authors of [28] performed sentiment analysis in short text answers with the use of a word embedding followed by two deep textual views: a CNN and a DSCNN, which is constructed with both a CNN layer and an LSTM layer. Their empirical evaluations proved that this method significantly improves predictive performance. Nevertheless, a few years ago, the authors of [29] introduced the attention concept to endow their translation method with the ability to give higher weight to more important words while taking care of the sequence structure of a text. Correspondingly, the authors of [30] implemented a hierarchical attention network for document classification, which consists of a word sequence encoder, a word-level attention layer, a sentence sequence encoder, and a sentence-level attention layer. Then, a new simple neural network architecture named the Transformer, based solely on attention mechanisms and dispensing with recurrence and convolutions entirely was proposed [31]. All these three attention neural networks are demonstrated to outperform other deep learning-based algorithms in sentiment analysis tasks. Presently, the authors of [32] introduced the Bidirectional Encoder Representations from Transformers (BERT) model, which is also based on the attention idea. The pre-trained BERT model can be fine-tuned with just one additional output layer to create simple and powerful algorithms and it obtained new state-of-the-art results on eleven different natural language processing tasks, including sentiment analysis. The authors of [33] proposed a movie recommender system that uses sentiment analysis data from Twitter, along with movie metadata and a social graph to recommend movies. Sentiment analysis provides information about how the audience responds to a particular movie and about how helpful this information is. The system used weighted score fusion to improve the recommendations. The authors of [17] proposed a user-based collaborative filtering approach that considers explicit ratings and sentiment analysis extracted from users' reviews. They use Matrix Factorization (MF) methods such as SVD, NMF, and SVD++ to predict explicit ratings, and use BERT, CNN, and LSTM to build the sentiment model. Results from the collaborative filtering approach and sentiment analysis are combined to generate integrated ratings and create a list of recommendations. However, inconsistent review-rating pairs can be very harmful and decrease the performance of the recommender system.