Deep Neural Network Architecture for Character-Level Learning on Short Text

Character-level deep learning for text classification tasks enables models to be trained without any prior knowledge of the data or language; however, an optimal neural network design for different text domains is not known and may vary. In this paper, we expand on current efforts to train neural networks from character-level data by conducting an experimental investigation on neural network design for text classification of short text documents. We trained and evaluated four networks, two consisting of convolutional layers followed by dense layers and two consisting of convolutional layers followed by a LSTM layer. Our experimental results show tweets need network architectures compatible with their short length. Networks found effective for other sentiment classification tasks may not produce an effective classifier in this domain, if their architecture is ill-suited for short instances.