Evolving Neural Networks for Text Classification using Genetic Algorithm-based Approaches

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Convolutional Neural Networks (CNNs) have been well-known for their promising performance in text classification and sentiment analysis because they can preserve the 1D spatial orientation of a document, where the sequence of words is essential. However, designing the network architecture of CNNs is by no means an easy task, since it requires domain knowledge from both the deep CNN and text classification areas, which are often not available and can increase operating costs for anyone wishing to implement this method. Furthermore, such domain knowledge is often different in different text classification problems. To resolve these issues, this paper proposes the use of Genetic Algorithm to automatically search for the optimal network architecture without requiring any intervention from experts. The proposed approach is applied on the IMDB dataset, and the experimental results show that it achieves competitive performance with the current state-of-the-art and manually-designed approaches in terms of accuracy, and also it requires only a few hours of training time.