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 by 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 deep
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 model. 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.

该研究提出了一种新的深度卷积神经网络模型以更有效地进行社交微博帖子中的口语文本情感分析，采用构建用户意见数据集，并使用不同的单词嵌入进行了评估，结果表明该模型具有 72％的准确性和有效性。