  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.
