# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 2 Background
### Sentiment Analysis in E-Commerce  
SA (Sentiment Analysis), also recognized as Opinion Mining (OM), is an activity of Natural Language Processing (NLP) and is process of extracting and evaluating peoples' opinions, sentiments, attitudes, and viewpoints toward particular entities, such as topics, products, and services [39]. Emerging internet-based applications such as social media platforms, the World Wide Web, blogs, etc., have led people to produce huge numbers of reviews of products, services, and other regular activities.  
Sentiment analysis is divided into two main approaches, lexicon-based and machine learning approaches. Each section is divided into other subsections, as Figure 2 shows.  
Sentiment analysis is divided into three main approaches to analyze the text, as shown in Figure 2 above. Most used techniques in the machine learning-based approach lie under the supervised learning type, whereas CNN-based techniques are used to observe behaviors and make a special connection while learning text. Finally, the semantic technique is also widely used as a sub-category of the lexicon corpus-based approach.  
Figure 2: Sentiment analysis approaches in e-commerce recommendations.  
Example of Studies Using Deep Learning in Sentiment Analysis  
We can describe the approaches that use sentiment analysis as follows:  
1. Bidirectional LSTM and Simple Embedding  
An extension of conventional LSTMs, called a bidirectional LSTM, can enhance model performance on sequential classification issues. Bidirectional LSTMs train two LSTMs rather than one on the input sequence in situations in which all style interface of the input pattern is known. The first is on an earlier version of the input sequence, and the next is on a reversed copy. This can give the network extra context and lead to a quicker and even more thorough learning process for the problem. In [39; 40; 41], LSTM has identified DL as the key feature extraction of the approach used in customer sentiment analysis.  
2. Convolutional Neural Networks (CNNs)  
Convolutional Neural Networks (CNNs) are machine intelligence or artificial intelligence (AI) algorithms built on multi-layer neural networks that can recognize and classify objects, as well as detect and segment objects in images. The benefit of CNNs over other classification algorithms (such as SVM, K-NN, Random-Forest, and others) would be that CNNs learn the best aspects to represent the artifacts in the images and have a high generalization capacity, being able to accurately classify new examples with only a small number of examples in the training set. In [40], CNN has identified DL as the key feature extraction component of the approach used in customer sentiment analysis.  
3. Average Pooling.  
To order to construct a down-sampled (pooled) feature map, the average value for regions of a feature map is calculated using the average pooling method. It is frequently applied following a convolutional layer. It adds a tiny bit of translation invariance, which means that changing the image's size slightly has little impact on the values of the majority of pooled outputs. In [42], average pooling has identified DL as the key feature extraction component of the approach used in customer sentiment analysis.  
4. Gated Recurrent Uni (GRU)  
[PERSON] et al. (2014) presented rectified linear units (GRUs) as a gating technique for recurrent neural networks. The GRU has a number of parameters fewer than an LSTM because it doesn't have an output gate, but it is similar to an LSTM with a forget gate. It was discovered that GRU and LSTM performed similarly on some polyphonic music modeling, speech signal modeling, and processing of natural language tasks. On some smaller, less frequently used datasets, GRUs have been proven to perform better. In [43] GRU has identified DL as the key feature extraction of the approach used in customer sentiment analysis.