The belief of Persian text mining based on deep learning with emotion-word separation

Abstract :

Belief analysis or the classification of texts based on the feelings and opinions of users on websites and social media helps people, companies and organizations to make important decisions. Belief mining includes a system for analyzing people's opinions and feelings about an entity such as products, people, organizations, according to the opinions, messages and tweets of users in social media. In this article, the belief analysis of Persian texts based on the messages, comments and tweets of users in social media and websites of 4 datasets using two deep learning methods, CNN, LSTM, taking into account the sense of the word, in two poles, positive and negative with intervals. 2- and 2+ are classified. In the proposed method, first the process of data pre-processing based on character to number conversion, removing the list of extra words and multi-word analysis is done, then for belief analysis and classification of Persian texts CNN, LSTM machine learning algorithm with word sense separation (WSD) is used to Recognize the intensity of emotions according to the words. We call the proposed model CNN_WSD and LSTM_WSD. In the proposed method, the Persian Twitter dataset is used for evaluation and then it is compared with other machine learning and deep learning methods, DNN, CNN, LSTM, in the implementation of this method, python software is used. The accuracy rate of the proposed method for LSTM-WSD and CNN-WSD is 95.8 and 94.3%, respectively.

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