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Convolutional neural networks are one of the most important and widely used constructs in natural language processing and AI in general. In many applications, they have achieved state-of-the-art performance, with training time faster than the other alternatives. However, due to their limited interpretability, they are less favored by practitioners over attention-based models, like RNNs and self-attention (Transformers), which can be visualized and interpreted more intuitively by analyzing the attention-weight heat-maps. In this work, we present a visualization technique that can be used to understand the inner workings of text-based CNN models. We also show how this method can be used to generate adversarial examples and learn the shortcomings of the training data.