Today, Social networks such as Twitter are the most widely used platforms for communication of people. Analyzing this data has useful information to recognize the opinion of people in tweets. Sentiment analysis plays a vital role in NLP, which identifies the opinion of the individuals about a specific topic. Natural language processing in Persian has many challenges despite the adventure of strong language models. The datasets available in Persian are generally in special topics such as products, foods, hotels, etc while users may use ironies, colloquial phrases in social media To overcome these challenges, there is a necessity for having a dataset of Persian sentiment analysis on Twitter. In this paper, we introduce the Exa sentiment analysis Persian dataset, which is collected from Persian tweets. This dataset contains 12,000 tweets, annotated by 5 native Persian taggers. The aforementioned data is labeled in 3 classes: positive, neutral and negative. We present the characteristics and statistics of this dataset and use the pre-trained Pars Bert and Roberta as the base model to evaluate this dataset. Our evaluation reached a 79.87 Macro F-score, which shows the model and data can be adequately valuable for a sentiment analysis system.

翻译：如今，推特等社交网络已成为人们最广泛使用的交流平台。分析此类数据对于识别用户在推文中的观点具有重要价值。情感分析作为自然语言处理（NLP）的关键任务，旨在识别个体针对特定主题所持的观点。尽管强大的语言模型不断涌现，波斯语的自然语言处理仍面临诸多挑战。现有的波斯语数据集通常局限于特定领域（如产品、食品、酒店等），而社交媒体用户常使用反讽、口语化表达等方式。为应对这些挑战，构建一个基于推特平台的波斯语情感分析数据集显得尤为必要。本文介绍了从波斯语推文收集构建的Exa情感分析波斯语数据集。该数据集包含12,000条推文，由5名波斯语母语标注者进行人工标注。数据标注采用三类情感标签：积极、中立与消极。我们详细阐述了该数据集的特征与统计信息，并基于预训练的Pars Bert和Roberta模型进行基准评估。实验结果显示，该数据集在宏观F值上达到79.87分，表明该模型与数据集能为情感分析系统提供充分有效的支持。