sentiment analysis of persian sentences using efficient deep learning in fiction

Text analysis has been one of the issues in recent research to identify users’ sentiments. most studies have identified sentiments’ positive and negative polarity in persian, and limited research has been done on analyzing emotions in persian sentences by covering the primary emotional states. in this study, first, a dataset of emotional sentences was prepared to label six basic emotional states, jamfa. this dataset contains 2350 sentences and (31222 words). this paper presents two models, efficient bert-bilstm(ebb) and xlm-r catboost(xlm-rc), that enhance the performance of the persian text emotion classification. this study has the advantages of human intelligence methods and statistical approaches to achieve better accuracy in sentence labeling. the evaluation indicates the accuracy of labeling is 92%, and the reliability of the dataset based on the type of emotions is 88%. the results show that the models at best achieved 86% accuracy in basic emotion classification and an 81% f-score in binary classification.