  Sentiment analysis is known as one of the most crucial tasks in the field of
natural language processing and Convolutional Neural Network (CNN) is one of
those prominent models that is commonly used for this aim. Although
convolutional neural networks have obtained remarkable results in recent years,
they are still confronted with some limitations. Firstly, they consider that
all words in a sentence have equal contributions in the sentence meaning
representation and are not able to extract informative words. Secondly, they
require a large number of training data to obtain considerable results while
they have many parameters that must be accurately adjusted. To this end, a
convolutional neural network integrated with a hierarchical attention layer is
proposed which is able to extract informative words and assign them higher
weight. Moreover, the effect of transfer learning that transfers knowledge
learned in the source domain to the target domain with the aim of improving the
performance is also explored. Based on the empirical results, the proposed
model not only has higher classification accuracy and can extract informative
words but also applying incremental transfer learning can significantly enhance
the classification performance.
