  In the Text Classification areas of Sentiment Analysis,
Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional Neural
Networks have gained special attention because of their performance and
accuracy. In this work, we applied recent advances in CNNs and propose a novel
architecture, Multiple Block Convolutional Highways (MBCH), which achieves
improved accuracy on multiple popular benchmark datasets, compared to previous
architectures. The MBCH is based on new techniques and architectures including
highway networks, DenseNet, batch normalization and bottleneck layers. In
addition, to cope with the limitations of existing pre-trained word vectors
which are used as inputs for the CNN, we propose a novel method, Improved Word
Vectors (IWV). The IWV improves the accuracy of CNNs which are used for text
classification tasks.
