Motivated and driven Electrical and Information Engineering graduate, specializing in Artificial Intelligence and Machine Learning. Demonstrates excellence in both academic pursuits and sports. Passionate about the potential of AI and ML to drive innovation. Committed to achieving success in these fields and dedicated to academic excellence. Recognized for a tenacious work ethic, proactive mindset, and a readiness to tackle complex challenges head-on.
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.
