Sentiment classification for telugu using transformed based approaches on a multi-domain dataset
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 trainin...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-05703-9 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Kannaiah Chattu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-97819503264640c58dc45e89dfd78aa9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-97819503264640c58dc45e89dfd78aa92025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115112110.1038/s41598-025-05703-9Sentiment classification for telugu using transformed based approaches on a multi-domain datasetKannaiah Chattu0K. Adi Narayana Reddy1Sai babu veesam2Pardha Saradhi Chirumamilla3Vunnava Dinesh Babu4Krishna Prakash5Shonak Bansal6Mohammad Rashed Iqbal Faruque7K. S. Al-mugren8Department of Computer Science & Engineering (AIML), Malla Reddy College of Engineering & TechnologyDepartment of Computer Science & Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education (IFHE)School of Computer Science and Engineering, VIT-AP UniversitySenior Software Engineer, Unicon Systems IncDepartment of CSE, RV Institute of TechnologyDepartment of Electronics and Communication, NRI Institute of TechnologyDepartment of Electronics and Communication Engineering, Chandigarh UniversitySpace Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangsaan MalaysiaPhysics department, Science College, Princess Nourah bint Abdulrahman UniversityAbstract 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.https://doi.org/10.1038/s41598-025-05703-9Sentiment classificationNatural Language processingTelugu LanguageTransformed based modelsXLM-RoBERTa |
| spellingShingle | 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 Sentiment classification for telugu using transformed based approaches on a multi-domain dataset Scientific Reports Sentiment classification Natural Language processing Telugu Language Transformed based models XLM-RoBERTa |
| title | Sentiment classification for telugu using transformed based approaches on a multi-domain dataset |
| title_full | Sentiment classification for telugu using transformed based approaches on a multi-domain dataset |
| title_fullStr | Sentiment classification for telugu using transformed based approaches on a multi-domain dataset |
| title_full_unstemmed | Sentiment classification for telugu using transformed based approaches on a multi-domain dataset |
| title_short | Sentiment classification for telugu using transformed based approaches on a multi-domain dataset |
| title_sort | sentiment classification for telugu using transformed based approaches on a multi domain dataset |
| topic | Sentiment classification Natural Language processing Telugu Language Transformed based models XLM-RoBERTa |
| url | https://doi.org/10.1038/s41598-025-05703-9 |
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