Boosting Sentiment Analysis in Persian Through a GAN-Based Synthetic Data Augmentation Method.

This paper presents a novel Sentiment Analysis (SA) dataset in the low-resource Persian language, including a data augmentation technique using Generative Adversarial Networks (GANs) to generate synthetic data, boosting the volume and variety of data for achieving stateof-the-art performance. We propose a novel annotated SA dataset, Senti-Persian, made of 67,743 public comments on movie reviews from Iranian websites (Namava, Filimo, and Aparat) and social media (YouTube, Twitter and Instagram). These reviews are labeled with one of the polarity labels, namely positive, negative, and neutral, by humans and later augmented. Our study includes a novel text augmentation model based on GANs. The generator was designed following the linguistic properties of Persian linguistics. In contrast, the discriminator was developed based on the cosine similarity of the vectorized original and generated sentences, i.e., using CLS-embeddings of BERT. An SA task was applied on both collected and augmented datasets, for which we observed a significant improvement in accuracy from 88.4% for the original dataset to 96% when augmented with synthetic data. The senti-Parsian dataset, including the original and the augmented ones, can be accessed on GitHub.