Deep Learning Models for Recommender System using Sentiment Analysis

In this article, we will explore a detailed overview of Deep Learning models for recommender system using sentiment analysis.

Deep learning models have recently found success in solving sentiment analysis problems at the sentence level and they could be used as the recommendation model in social applications.

In this article, I will demonstrate deep learning models to solve one of the most important problems in recommender systems – sentiment analysis through exploring 2 different datasets with 2 different approaches (e.g., fully connected neural network, convolutional neural network).

Detailed steps and useful advice on how to integrate sentiment analysis into a recommender system are also provided in this article as well as links to relevant source codes on Github.

Why Deep Learning?

Deep Learning is a field of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, where each layer uses unsupervised learning to generate representations of concepts from lower levels.

Deep Learning has produced results superior to traditional machine-learning techniques on numerous tasks.

Due to its flexibility and wide applicability, deep neural networks can be used as feature extractors for many other Machine Learning algorithms such as Recommendation Systems and Sentiment Analysis.

What Is Deep Learning Sentiment Analysis?

Deep Learning is an ensemble of different kinds of neural networks, inspired by the structure and function of a biological brain.

These models are used to solve complex pattern recognition problems, including image recognition and machine translation.

Deep Learning Sentiment Analysis is applied on recommender systems, where you try to predict which item or items a user would like best from your product catalogue.

This process can be divided into two main steps: first, we need to collect data about users’ behaviour (clicks, purchases) and their preferences; second, we use it as input data for our model that will be able to output predictions about user preferences.

A good example of Deep Learning sentiment analysis in action is Google’s Knowledge Graph project – they analyze millions of queries every day to provide more relevant search results.

They have also implemented Deep Learning algorithms in their search engine algorithm with promising results.

Related Article: What is an Artificial Neural Network (ANN)?

Recommendations in Social Applications

A recommender system is a class of machine-learning algorithms that learn to predict an optimal action given a set of observations.

A social recommendation framework refers to computing recommendations by coupling user behavioural information and collaborative aspects in online media platforms.

Using applying deep neural networks, deep learning methods enable us to design data-driven approaches with high precision and low computational cost.

Deep neural networks deliver extraordinary performance even on big datasets with millions of users, millions of items and millions of ratings.

Deep Learning Model 1 – Convolutional Neural Network

1. The input layer:

 The convolutional layer is in charge of performing convolutions over our feature maps. Convolutional layers are identical to normal neural networks, except that they apply their parameters over all of their channels, instead of having a specific set of channels.

2. Convolutional Layers: 

The first convolutional layer is going to take in an input of 32, as we are dealing with images. After convolving over all its channels, it will produce a feature map of size 32×32.

We will then add a max-pooling layer which reduces our image size to 16×16. Following that, we add another convolutional layer which produces an output of 16x16x2048.

3. Attention Layer:

Attention provides an interface that connects the encoder and decoder. This framework allows the model to work on key aspects of the input sequence and make connections between different components.

4. Final Fully Connected Layer:

A fully convolutional network is an architecture for semantic segmentation, which is characterized by wholly locally connected layers, like convolution, pooling, and upsampling. Train faster by limiting the number of densely connected layers.

5. Loss Function and Optimizer:

Here we are going to use a cross-entropy loss function. To train our model, we will be using an optimizer that is a variant of Adam. We will be using a learning rate of 0.0001 and a momentum of 0.9.

6. Validation and Test Set/Train Set Split:

We will validate our model using a test set. We’re going to break our data down into two subsets, a training set, and a validation set. We’ll leave 60% for the training set and take 40% for the validation set.

Related Article: Top 10 Computer Vision Tools in 2022

Model 2 – Sequential Convolutional Neural Network (Seq-CNN)

Attention-based Seq-CNN model, by Kyunghyun Cho et al. at Google DeepMind, is an enhanced version of Seq-CNN which takes advantage of the attention mechanism and encoder-decoder architecture.

We’ll use this model to build our deep learning-based solution to solve recommendation tasks. The following figure shows the overall structure of Seq-CNN with the LSTM (Long Short Term Memory) recurrent neural network used as the decoder part.

The main difference between regular CNN and Seq-CNN is that in the former case input sequences are fed into fully connected layers whereas in the latter case they are fed into LSTM layers firstly and then passed through convolutional layers as usual.

Related Article: Autoencoders: Introduction to Neural Networks

Model 3 – Recurrent Neural Network (RNN)

Recurrent neural networks (RNNs) are a special type of feed-forward neural network that is capable of modelling sequential data.

This means that an RNN can process inputs with a temporal structure, like text or audio. Recurrent neural networks consist of two parts, one called an encoder and another called a decoder.

Both consider information from previous timesteps when processing current timestep input data. In other words, recurrent neural networks can remember things over time.

The encoder uses Long Short-Term Memory units (LSTMs), which are good at capturing long term dependencies in sequences.

The decoder uses traditional fully connected layers. In addition, both LSTM layers and traditional fully connected layers can be stacked together inside an RNN layer as well.

Therefore, you can stack LSTM layers on top of each other to increase memory capacity if needed.

Model 4 – LSTM

To tackle sentiment classification, we will use LSTMs to consider and capture long-term dependencies.

We will also use them to capture local character n-grams from our feature maps. Long short-term memory units are a type of recurrent neural network (RNN) that allow information to persist across time steps by creating internal memory states.

By doing so, they can learn long term dependencies within sequences of data. In particular, LSTMs can be used as a method of deep learning to model complex interactions between features in sequential data.

For example, consider a case where you have an input sequence such as The quick brown fox jumps over the lazy dog. The word fox is dependent on brown because it is preceded by it.

Neural Network Model for Deep Learning In Recommender System

The neural network is an artificial neural network, made up of layers of nodes (or neurons) and different connections between these nodes.

The neural network model follows several steps in which an input image is transformed into a corresponding output image.

We have considered 3 main steps to improve deep learning models in recommender systems, on one hand, we create a word embedding model based on pre-trained GloVe word embedding, meanwhile, we provide deep convolutional networks to remove irrelevant attributes to achieve more accurate results.

Related Article: What is Backpropagation in Neural Network?

Steps to implement recommender system using sentiment analysis

1. Problem Definition:

 Recommendation systems generate up-to-date personalized recommendations that suggest products to users based on their tastes and preferences. Recommenders work as smart filters and assist people in identifying items or services from huge information spaces that will be relevant to them.

2. Data Collection: 

Sentiment Analysis is a process of extracting subjective information from text or speech. It can be used to determine how a person feels about an object, event, or activity by identifying and categorizing expressions in either written or spoken language.

3. Data Preprocessing: 

Text data is a sequence of words, and we need to convert it into a vector representation before feeding it to any deep learning model.

The process of converting text into numerical form is called word embedding or word2vec. Word2vec is an unsupervised method that learns distributed representations of words from large unlabeled corpora.

4. Model Building: 

Deep learning is a class of machine learning algorithms that use artificial neural networks to model high-level abstractions in data.

Deep neural networks are composed of multiple processing layers, where each layer uses simple algorithms to learn increasingly complex representations of input data.

5. Results Analysis: 

The results of deep learning algorithms can be evaluated by looking at their accuracy, computational efficiency, and generalization ability.

Accuracy is a measure of how well an algorithm performs against a gold standard dataset. Computational efficiency is a measure of how fast an algorithm takes to train on a given dataset and how much memory it uses while doing so.

Generalization ability refers to whether or not an algorithm will perform well on data that it has never seen before.

Conclusion

Recommendations, in social applications, determine what content a user will see and when he sees it. A good recommendation engine can increase engagement and retention of users as well as boost revenue per user.

Deep Learning is an area of machine learning research that has been introduced with a focus on end-to-end deep neural networks that are composed of multiple non-linear transformations applied to raw input data.

The main reason to consider deep learning as a means of building a recommendation engine is because of its potential to significantly improve upon existing recommendation engines by producing more accurate predictions, decreasing training and production costs, and also helping with privacy concerns. Deep Learning is also currently one of the state-of-the-art technologies in text classification problems such as sentiment analysis.