Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models
This work highlights critical data gaps for Farsi NLP, impacting over 127 million speakers, but is incremental as it surveys existing issues without proposing new solutions.
The study analyzed subjective tasks like sentiment analysis in Farsi, revealing significant data challenges and unstable model performance, with findings indicating that current data volume is insufficient for substantial NLP improvements.
Given Farsi's speaker base of over 127 million people and the growing availability of digital text, including more than 1.3 million articles on Wikipedia, it is considered a middle-resource language. However, this label quickly crumbles when the situation is examined more closely. We focus on three subjective tasks (Sentiment Analysis, Emotion Analysis, and Toxicity Detection) and find significant challenges in data availability and quality, despite the overall increase in data availability. We review 110 publications on subjective tasks in Farsi and observe a lack of publicly available datasets. Furthermore, existing datasets often lack essential demographic factors, such as age and gender, that are crucial for accurately modeling subjectivity in language. When evaluating prediction models using the few available datasets, the results are highly unstable across both datasets and models. Our findings indicate that the volume of data is insufficient to significantly improve a language's prospects in NLP.