Aspect-based sentiment Analysis (ABSA) is an advanced NLP task that identifies sentiments related to specific aspects of a product or service, offering more detailed consumer insights than general sentiment analysis. The proposed research introduces a hybrid model, Instruct-DeBERTa, which combines InstructABSA for aspect term extraction (ATE) and DeBERTa-V3-baseabsa-V1 for aspect sentiment classification (ASC). Using datasets from SemEval Restaurant 2014,2015,2016 and SemEval Laptop 2014, the model demonstrated improved performance across domains. Further enhancements included category classification using cosine similarity, linear layers with ReLU activation, regularization methods, and optimized attention heads for the hospitality domain. These improvements address existing model limitations, providing a comprehensive solution for analyzing consumer feedback, valuable for enhancing customer satisfaction and product development.
Keywords: Natural Language Processing | Aspect-based Sentiment Analysis | Aspect Extraction | Pre-trained Models | Classification | Sentiment Analysis | Textual Reviews |
@inproceedings{jayakody2024instruct2,
title={{Instruct-DeBERTa: A Hybrid Approach for Enhanced Aspect-Based Sentiment Analysis with Category Extraction}},
author={Jayakody, Dineth and Isuranda, Koshila and Malkith, A V A and de Silva, Nisansa and Sandamali, G G N and Sudheera, K L K and Rajith, Sachintha},
booktitle={Eighth Widening NLP Workshop (WiNLP 2024) Phase II},
abstract={Aspect-based sentiment Analysis (ABSA) is an advanced NLP task that identifies sentiments related to specific aspects of a product or service, offering more detailed consumer insights than general sentiment analysis. The proposed research introduces a hybrid model, Instruct-DeBERTa, which combines InstructABSA for aspect term extraction (ATE) and DeBERTa-V3-baseabsa-V1 for aspect sentiment classification (ASC). Using datasets from SemEval Restaurant 2014,2015,2016 and SemEval Laptop 2014, the model demonstrated improved performance across domains. Further enhancements included category classification using cosine similarity, linear layers with ReLU activation, regularization methods, and optimized attention heads for the hospitality domain. These improvements address existing model limitations, providing a comprehensive solution for analyzing consumer feedback, valuable for enhancing customer satisfaction and product development.},
year={2024},
url={https://openreview.net/pdf?id=DS6THG8s9w},
misc={https://openreview.net/pdf?id=DS6THG8s9w,NLP,https://goo.gl/iY6aTr}
}
ActivePI: 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.
ActivePI: 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.