The lack of a suitable tool for the analysis of conversational texts in the Persian language has made various analyses of these texts, including Sentiment Analysis, difficult. In this research, we tried to make the understanding of these texts easier for the machine by providing PSC, Persian Slang Converter, a tool for converting conversational texts into formal ones, and by using the most up-to-date and best deep learning methods along with the PSC, the sentiment learning of short Persian language texts for the machine in a better way. be made More than 10 million unlabeled texts from various social networks and movie subtitles (as Conversational texts) and about 10 million news texts (as formal texts) have been used for training unsupervised models and formal implementation of the tool. 60,000 texts from the comments of Instagram social network users with positive, negative, and neutral labels are considered supervised data for training the emotion classification model of short texts. Using the formal tool, 57% of the words of the corpus of conversation were converted. Finally, by using the formalizer, FastText model, and deep LSTM network, an accuracy of 81.91 was obtained on the test data.

使用Persian Slang Converter(PSC)工具和最新的深度学习方法，本研究旨在为机器提供转换会话文本为正式文本的工具。通过训练无监督模型和正式实施，从各种社交网络和电影字幕中获得超过1000万的未标记文本和1000万新闻文本，还使用来自Instagram社交网络用户评论的正面、负面和中性标签的60000条文本训练短文本情感分类模型，最终在测试数据上达到了81.91%的准确率。

波斯俚语文本转换为正式文本，并在社交媒体上进行波斯简短文本的深度学习以进行情感分类