B.Sc. (Hons) Engineering graduate with First Class Honours and strong professional experience as a Software Engineer. Skilled in Software Development, AI, and ML, with a passion for research and development to drive innovative and high-quality solutions. I adapt quickly to new technologies, communicate and collaborate effectively, and approach challenges with a results-driven mindset. Passionate about continuous learning, I aim to contribute meaningfully to projects and drive impactful outcomes within dynamic teams.
Keywords: Natural Language Processing | Aspect-based Sentiment Analysis | Aspect Extraction | DeBERTa | InstructABSA | Sentiment Analysis |
Journal Papers
Dineth Jayakody, A V A Malkith, Koshila Isuranda, Vishal Thenuwara, Nisansa de Silva, Sachintha Rajith Ponnamperuma, G G N Sandamali, and K L K Sudheera, "Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews", ICTer, vol. 18, no. 2, pp. 39-50, 2025. doi: 10.4038/icter.v18i2.7290
Conference Papers
Dineth Jayakody, Koshila Isuranda, A V A Malkith, Nisansa de Silva, Sachintha Rajith Ponnamperuma, G G N Sandamali, K L K Sudheera, and Kashnika Gimhani Sarathchandra, "Enhanced Aspect-Based Sentiment Analysis with Integrated Category Extraction for Instruct-DeBERTa", in Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation, 2024, pp. 665--674.
Dineth Jayakody, Koshila Isuranda, A V A Malkith, Nisansa de Silva, Sachintha Rajith Ponnamperuma, G G N Sandamali, and K L K Sudheera, "Aspect-Based Sentiment Analysis Techniques: A Comparative Study", in 2024 Moratuwa Engineering Research Conference (MERCon), 2024, pp. 205--210. doi: 10.1109/MERCon63886.2024.10688631
Workshop Papers
Dineth Jayakody, Koshila Isuranda, A V A Malkith, Nisansa de Silva, G G N Sandamali, K L K Sudheera, and Sachintha Rajith, "Instruct-DeBERTa: A Hybrid Approach for Enhanced Aspect-Based Sentiment Analysis with Category Extraction", in Eighth Widening NLP Workshop (WiNLP 2024) Phase II, 2024.
PI: Nisansa de Silva
We propose to advance aspect-based sentiment analysis (ABSA) by developing enhanced models, datasets, and evaluation methods that improve the accuracy and applicability of sentiment classification across multiple domains.
PI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
