  Convolutional neural networks have been successfully applied to various NLP
tasks. However, it is not obvious whether they model different linguistic
patterns such as negation, intensification, and clause compositionality to help
the decision-making process. In this paper, we apply visualization techniques
to observe how the model can capture different linguistic features and how
these features can affect the performance of the model. Later on, we try to
identify the model errors and their sources. We believe that interpreting CNNs
is the first step to understand the underlying semantic features which can
raise awareness to further improve the performance and explainability of CNN
models.
