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Sentiment analysis is known as one of the most crucial tasks in the field ofnatural language processing and Convolutional Neural Network (CNN) is one ofthose prominent models that is commonly used for this aim. Althoughconvolutional neural networks have obtained remarkable results in recent years,they are still confronted with some limitations. Firstly, they consider thatall words in a sentence have equal contributions in the sentence meaningrepresentation and are not able to extract informative words. Secondly, theyrequire a large number of training data to obtain considerable results whilethey have many parameters that must be accurately adjusted. To this end, aconvolutional neural network integrated with a hierarchical attention layer isproposed which is able to extract informative words and assign them higherweight. Moreover, the effect of transfer learning that transfers knowledgelearned in the source domain to the target domain with the aim of improving theperformance is also explored. Based on the empirical results, the proposedmodel not only has higher classification accuracy and can extract informativewords but also applying incremental transfer learning can significantly enhancethe classification performance.

A Novel Deep Learning Method for Textual Sentiment Analysis