Sentiment classification for telugu using transformed based approaches on a multi-domain dataset - Kannaiah Chattu; K. Adi Narayana Reddy; Sai babu veesam; Pardha Saradhi Chirumamilla; Vunnava Dinesh Babu; Krishna Prakash; Shonak Bansal; Mohammad Rashed Iqbal Faruque; K. S. Al-mugren (Scientific Reports)
Abstract Sentiment analysis is an essential component of Natural Language Processing (NLP) in resource-abundant languages such as English. Nevertheless, poor-resource languages such as Telugu have experienced limited efforts owing to multiple considerations, such as a scarcity of corpora for training machine learning models and an absence of gold standard datasets for evaluation. The current surge of transformed based models in NLP enables the attainment of exceptional performance in many different tasks. Nevertheless, researchers are increasingly interested in exploring the potential of transformed based models that have been pre-trained in several languages for various natural language processing applications, particularly for languages with limited resources. This research examines the efficacy of four pre-trained transformed based models, specifically IndicBERT, RoBERTa, DeBERTa, and XLM-RoBERTa, for sentence-level sentiment analysis in the Telugu language. Evaluated the performance of all four models using our dataset, “Sentikanna,” which consists of numerous domain datasets for the Telugu language. We compared the performance of these models with three different datasets and observed a promising outcome. XLM-RoBERTa achieves a good accuracy of 79.42% for a binary sentiment classification. This work can be considered a reliable standard for sentiment analysis in the Telugu language.