A Deep Learning Approach for Extracting Polarity from Customers' Reviews

Due to the expansion of social networks and media such as Tweeter, Facebook, LinkedIn, and different weblogs, and the great increase in information sharing and comments, Which typically are in the form of text data, big enough to be recognized as big data., and with respect to the importance of these data for the analysis of customers' priorities, needs and their attitudes toward different products, finding and extracting data from their comments, are the primary goals of this research. To serve this purpose, this research has used deep learning approach, and multilayer neural network methods in order to extract the polarity of customers' opinions and comments in two domains of products/services ranging from restaurant to laptop. The findings of this study indicate that the proposed model using the potencies of the long short-term-memory networks, is able to determine the comments' polarity with 85 % and 84.62 % precision for restaurant and laptop domains respectively, in such a way that the results are relatively more accurate than the results of other methods