Applying Natural Language Processing Techniques for Modeling Political Events Using Deep Neural Networks

Today, due to the spread of social networks and increasing the number of users, the use of information in these networks has become one of the most important issues in various fields. Using natural language processing techniques for political analysis can be one of the effective ways to improve the goals of different societies in the field of politics.In this research to predict the users’ political tendency, a proposed deep learning model and Twitter’s text data are used. The aim is to present a suitable model with high accuracy to predict the vote results in English and Persian languages. The proposed approach has been tested on the dataset collected in Persian and English from the Twitter social network and the results obtained have been compared with the results of the existing methods.The proposed model consists of a deep convolutional neural network with long short-term memory (CNN-BiLSTM) in such a way that it simultaneously learns the semantic relation of the text and uses the advantages of CNN and BiLSTM deep networks simultaneously. The proposed algorithm has achieved 84.5% accuracy for Persian data and 96.18% accuracy for English data.