  Introduction: Microblogging websites have massed rich data sources for
sentiment analysis and opinion mining. In this regard, sentiment classification
has frequently proven inefficient because microblog posts typically lack
syntactically consistent terms and representatives since users on these social
networks do not like to write lengthy statements. Also, there are some
limitations to low-resource languages. The Persian language has exceptional
characteristics and demands unique annotated data and models for the sentiment
analysis task, which are distinctive from text features within the English
dialect. Method: This paper first constructs a user opinion dataset called
ITRC-Opinion in a collaborative environment and insource way. Our dataset
contains 60,000 informal and colloquial Persian texts from social microblogs
such as Twitter and Instagram. Second, this study proposes a new architecture
based on the convolutional neural network (CNN) model for more effective
sentiment analysis of colloquial text in social microblog posts. The
constructed datasets are used to evaluate the presented architecture.
Furthermore, some models, such as LSTM, CNN-RNN, BiLSTM, and BiGRU with
different word embeddings, including Fasttext, Glove, and Word2vec,
investigated our dataset and evaluated the results. Results: The results
demonstrate the benefit of our dataset and the proposed model (72% accuracy),
displaying meaningful improvement in sentiment classification performance.
