This paper presents a novel 1-D sentiment classifier trained on the benchmark IMDB dataset. The classifier is a 1-D convolutional neural network with repeated convolution and max pooling layers. The main contribution of this work is the demonstration of a deconvolution technique for 1-D convolutional neural networks that is agnostic to specific architecture types. This deconvolution technique enables text classification to be explained, a feature that is important for NLP-based decision support systems, as well as being an invaluable diagnostic tool.