Exa-PSD: a new Persian sentiment analysis dataset on Twitter
This provides a dataset for Persian sentiment analysis on social media, addressing domain-specific challenges like irony and colloquial language, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of a Persian sentiment analysis dataset for Twitter by introducing Exa-PSD, a dataset of 12,000 tweets annotated into three classes, and achieved a 79.87 Macro F-score using pre-trained models.
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.