Belief mining and the study of public opinion provide valuable information. Analyzing the feelings and belief mining of social media data leads to understanding users' opinions and has wide applications in decision making and policymaking. This article applies a new method based on deep learning to solve the problems of belief mining for Persian comments on the Twitter. In this method, first, the data is preprocessed with a deep neural network and then classified into political, cultural, economic, and sports classes, and the sentimental polarity is obtained. SentiPers is applied on four different datasets from Persian Twitter, Digikala store, Google translator, and synonym for evaluation. Then the results are compared with other machine learning and deep learning methods such as neural network, support vector machine, DNN, CNN, and LSTM. Python software has been used to implement this method. The accuracy of the proposed word embedding method for LSTM, CNN, DNN on the SentiPres dataset is 0.931, 0.923, 0.916 respectively. For the TF-IDF method, it is 0.837, 0.863, 0.883 respectively. that the accuracy of LSTM-WSD, CNN-WSD model has increased by 8% and 6% compared to TF-IDF. The results show that the LSTM and Word embedding methods work best.

中文翻译：

---

基于深度学习和用户意见的波斯语文本信念挖掘


信念挖掘和舆论研究提供了有价值的信息。分析社交媒体数据的感受和信念挖掘可以了解用户的意见，并在决策和政策制定中具有广泛的应用。本文应用一种基于深度学习的新方法来解决Twitter上波斯语评论的信念挖掘问题。在该方法中，首先用深度神经网络对数据进行预处理，然后将数据分为政治、文化、经济和体育类别，并获得情感极性。 SentiPers 应用在波斯语 Twitter、Digikala 商店、谷歌翻译和同义词的四个不同数据集上进行评估。然后将结果与神经网络、支持向量机、DNN、CNN、LSTM等其他机器学习和深度学习方法进行比较。使用Python软件来实现该方法。所提出的 LSTM、CNN、DNN 词嵌入方法在 SentiPres 数据集上的准确率分别为 0.931、0.923、0.916。对于TF-IDF方法，分别为0.837、0.863、0.883。 LSTM-WSD、CNN-WSD模型的准确率相比TF-IDF分别提高了8%和6%。结果表明 LSTM 和 Word embedding 方法效果最好。