Training Convolutional Networks on Truncated Text

Classifiers trained using deep neural networks have been shown to be effective for a wide variety of classification tasks including text sentiment. One such approach is to use convolutional neural networks to learn from character-level representations of documents. This approach is appealing as all feature engineering, extraction or selection is performed by the neural network, automatically generating high level abstract representations of the data. With character-level learning, network topology is dependent on document length as this determines the input shape for the network. In this paper, we investigate how limiting the number of characters used from a document impacts performance and compare neural network performance against a Multinomial Naive Bayes baseline. Our results show the required number of characters is linked to the document domain and neural network performance exceeds that of Multinomial Naive Bayes; however, too low of a number results in performance degradation.