Leveraging Arabic sentiment classification using an enhanced CNN-LSTM approach and effective Arabic text preparation

The high variety in the forms of the Arabic words creates significant complexity related challenges in Natural Language Processing (NLP) tasks for Arabic text. These challenges can be dealt with by using different techniques for semantic representation, such as word embedding methods. In addition, approaches for reducing the diversity in Arabic morphologies can also be employed, for example using appropriate word normalisation for Arabic texts. Deep learning has proven to be very popular in solving different NLP tasks in recent years as well. This paper proposes an approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to improve sentiment classification, by excluding the max-pooling layer from the CNN. This layer reduces the length of generated feature vectors after convolving the filters on the input data. As such, the LSTM networks will receive well-captured vectors from the feature maps. In addition, the paper investigated different effective approaches for preparing and representing the text features in order to increase the accuracy of Arabic sentiment classification.