@article{lee2026dimabsa,
  title={DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis},
  author={Lee, Lung-Hao and Yu, Liang-Chih and Loukashevich, Natalia and Alimova, Ilseyar and Panchenko, Alexander and Lin, Tzu-Mi and Xu, Zhe-Yu and Zhou, Jian-Yu and Zheng, Guangmin and Wang, Jin and others},
  journal={arXiv preprint arXiv:2601.23022},
  year={2026}
}

@article{russell1980circumplex,
  title     = {A circumplex model of affect},
  author    = {Russell, James A},
  journal   = {Journal of Personality and Social Psychology},
  volume    = {39},
  number    = {6},
  pages     = {1161--1178},
  year      = {1980},
  publisher = {American Psychological Association}
}



@inproceedings{peng-etal-2020-knowing,
  title     = {Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis},
  author    = {Peng, Haiyun and Xu, Lu and Bing, Lidong and Huang, Fei and Lu, Wei and Si, Luo},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {34},
  number    = {05},
  pages     = {8600--8607},
  year      = {2020}
}

@inproceedings{d2e2s2023,
  title={Dual encoder: Exploiting the potential of syntactic and semantic for aspect sentiment triplet extraction},
  author={Zhao, Xiaowei and Zhou, Yong and Xu, Xiujuan},
  booktitle={Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024)},
  pages={5401--5413},
  year={2024},
  url={https://github.com/TYZY89/D2E2S}
}

@inproceedings{he2021debertav3,
  title     = {{D}e{BERT}a{V}3: Improving {D}e{BERT}a using {ELECTRA}-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
  author    = {He, Pengcheng and Gao, Jianfeng and Chen, Weizhu},
  booktitle = {International Conference on Learning Representations},
  year      = {2023}
}

@article{warriner2013norms,
  title     = {Norms of valence, arousal, and dominance for 13,915 {E}nglish lemmas},
  author    = {Warriner, Amy Beth and Kuperman, Victor and Brysbaert, Marc},
  journal   = {Behavior Research Methods},
  volume    = {45},
  number    = {4},
  pages     = {1191--1207},
  year      = {2013},
  publisher = {Springer}
}

@inproceedings{thenuwara2025dess,
  title={DESS: DeBERTa Enhanced Syntactic-Semantic Aspect Sentiment Triplet Extraction},
  author={Thenuwara, Vishal and de Silva, Nisansa},
  booktitle={International Conference on Computational Collective Intelligence},
  pages={185--199},
  year={2025},
  organization={Springer}
}

@inproceedings{de2025geesanbhava,
  title={{GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set}},
  author={De Mel, Yomal and de Silva, Nisansa},
  booktitle={International Conference on Computational Collective Intelligence},
  pages={157--171},
  year={2025},
  organization={Springer}
}


@article{jayakody2024instruct,
  title={{Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews}},
  author={Jayakody, Dineth and Malkith, A V A and Isuranda, Koshila and Thenuwara, Vishal and de Silva, Nisansa and Ponnamperuma, Sachintha Rajith and Sandamali, G G N and Sudheera, K L K},
  journal={ICTer},
abstract={Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) that focuses on extracting sentiments related to specific aspects within a text, offering deep insights into customer opinions. Traditional sentiment analysis methods, while useful for determining overall sentiment, often miss the implicit opinions about particular product or service features. This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning and deep learning techniques. We emphasize the recent advancements in Transformer-based models, particularly Bidirectional Encoder Representations from Transformers (BERT) and its variants, which have set new benchmarks in ABSA tasks. We focused on finetuning Llama and Mistral models, building hybrid models using the SetFit framework, and developing our own model by exploiting the strengths of state-of-the-art (SOTA) Transformer-based models for aspect term extraction (ATE) and aspect sentiment classification (ASC). Our hybrid model Instruct - DeBERTa uses SOTA InstructABSA for aspect extraction and DeBERTa-V3-baseabsa-V1 for aspect sentiment classification. We utilize datasets from different domains to evaluate our model's performance. Our experiments indicate that the proposed hybrid model significantly improves the accuracy and reliability of sentiment analysis across all experimented domains. As per our findings, our hybrid model Instruct - DeBERTa is the best-performing model for the joint task of ATE and ASC for both SemEval restaurant 2014 and SemEval laptop 2014 datasets separately. By addressing the limitations of existing methodologies, our approach provides a robust solution for understanding detailed consumer feedback, thus offering valuable insights for businesses aiming to enhance customer satisfaction and product development.},
  year={2025},
 volume={18},
  number={2},
doi={10.4038/icter.v18i2.7290},
pages={39-50},
misc={https://arxiv.org/abs/2408.13202,NLP,https://goo.gl/iY6aTr}
}


% [change.3] Added task organizer system paper citation for baseline comparison.
% Cited as: \cite{yu-etal-2026-semeval}
@inproceedings{yu-etal-2026-semeval,
  title     = {{SemEval}-2026 Task 3: {DimABSA} -- Dimensional Aspect-Based Sentiment Analysis},
  author    = {Yu, Liang-Chih and Lee, Lung-Hao and others},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)},
  year      = {2026},
  address   = {Vienna, Austria},
  publisher = {Association for Computational Linguistics},
  note      = {To appear}
}


@inproceedings{gunathilaka2022aspect,
  title={Aspect-based Sentiment Analysis on Mobile Application Reviews},
  author={Gunathilaka, Sadeep and De Silva, Nisansa},
  booktitle={2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer)},
  pages={183--188},
  year={2022},
  organization={IEEE},
  abstract={With the popularity of smartphones, mobile application (A.K.A Mobile App) development has become a booming industry all across the World. One of the main hurdles that app developers are facing, is understanding users' needs and catering their products to satisfy the users. Though Users are one of the main stakeholders of the App development process it is harder to incorporate them into the requirement elicitation process. Numerous studies have shown that incorporating user reviews in the requirement elicitation process paves the way to a better understanding of user needs which, in turn, helps developers develop better apps that satisfy the targeted audience of the app. In this paper, we introduce a CNN-based approach to analyze user reviews using Aspect-based Sentiment Analysis (ABSA). The results show that our approach could achieve 87.88\%, 93.75\%, and 31.25\% improvements in aspect category classification and 16.43\%, 23.35\%, and 3.72\% improvements in aspect sentiment classification over the baseline results for AWER dataset in productivity, social networking, and game domains respectively.},
  doi={10.1109/ICTer58063.2022.10024070},
  misc={https://ieeexplore.ieee.org/abstract/document/10024070,NLP,https://goo.gl/iY6aTr}
}

@inproceedings{jayakody2024aspect,
  title={{Aspect-Based Sentiment Analysis Techniques: A Comparative Study}},
  author={Jayakody, Dineth and Isuranda, Koshila and Malkith, A V A and de Silva, Nisansa and Ponnamperuma, Sachintha Rajith and Sandamali, G G N and Sudheera, K L K},
booktitle={2024 Moratuwa Engineering Research Conference (MERCon)},
  pages={205--210},
  year={2024},
organization={IEEE},
doi={10.1109/MERCon63886.2024.10688631},
 abstract={Since the dawn of the digitalisation era, customer feedback and online reviews are unequivocally major sources of insights for businesses. Consequently, conducting comparative analyses of such sources has become the de facto modus operandi of any business that wishes to give itself a competitive edge over its peers and improve customer loyalty. Sentiment analysis is one such method instrumental in gauging public interest, exposing market trends, and analysing competitors. While traditional sentiment analysis focuses on overall sentiment, as the needs advance with time, it has become important to explore public opinions and sentiments on various specific subjects, products and services mentioned in the reviews on a finer-granular level. To this end, Aspect-based Sentiment Analysis (ABSA), supported by advances in Artificial Intelligence (AI) techniques which have contributed to a paradigm shift from simple word-level analysis to tone and context-aware analyses, focuses on identifying specific aspects within the text and determining the sentiment associated with each aspect. In this study, we compare several deep-NN methods for ABSA on two benchmark datasets (Restaurant14 and Laptop-14) and found that FAST LSA obtains the best overall results of 87.6\% and 82.6\% accuracy but does not pass LSA+DeBERTa which reports 90.33\% and 86.21\% accuracy respectively.},
misc={https://arxiv.org/abs/2407.02834,NLP,https://goo.gl/iY6aTr}
}


@inproceedings{mudalige2020sigmalaw,
  title={{SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts}},
  author={Mudalige, Chanika Ruchini and Karunarathna, Dilini and Rajapaksha, Isanka and de Silva, Nisansa and Ratnayaka, Gathika and Perera, Amal Shehan and Pathirana, Ramesh},
  booktitle={2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)},
  pages={488--493},
  year={2020},
  organization={IEEE},
  abstract={Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing research over many different domains, but it is not widely discussed in the legal domain. A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA. To the best of our knowledge, there is no publicly available dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion texts. Therefore, creating a publicly available dataset for the research of ABSA for the legal domain can be considered as a task with significant importance. In this study, we introduce a manually annotated legal opinion text dataset (SigmaLaw-ABSA) intended towards facilitating researchers for ABSA tasks in the legal domain. SigmaLaw-ABSA consists of legal opinion texts in the English language which have been annotated by human judges. This study discusses the sub-tasks of ABSA relevant to the legal domain and how to use the dataset to perform them. This paper also describes the statistics of the dataset and as a baseline, we present some results on the performance of some existing deep learning based systems on the SigmaLaw-ABSA dataset.},
  misc={https://bit.ly/3nLx6TA,NLP:ML:LAW,https://i1.rgstatic.net/publication/345788498_SigmaLaw-ABSA_Dataset_for_Aspect-Based_Sentiment_Analysis_in_Legal_Opinion_Texts/links/5fc5ebeb299bf1a422c76d1a/largepreview.png},
  doi={10.1109/ICIIS51140.2020.9342650}
}

@inproceedings{rajapaksha2020rule,
  title={{Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts}},
  author={Rajapaksha, Isanka and Mudalige, Chanika Ruchini and Karunarathna, Dilini and de Silva, Nisansa and Rathnayaka, Gathika and Perera, Amal Shehan},
  booktitle={2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer)},
  pages={284--285},
  year={2020},
  organization={IEEE},
  abstract={Aspect Based Sentiment Analysis (ABSA) deals with extracting aspects from a given text and then allocate each aspect a sentiment level (positive, negative or neutral) [1]. A number of researchers have addressed ABSA in different domains except for the legal domain. In this study, we explore how the concepts related to aspect-based sentiment analysis can be used in the legal domain to extract valuable information from legal opinion text. To this regard, we propose a rule based approach to perform aspect-based sentiment analysis in order to figure out the sentiment level of a sentence in relation to each legal party related to a court case (considering the legal parties as the aspects).},
  misc={https://bit.ly/2USPmxO,NLP:ML:LAW,https://i1.rgstatic.net/publication/345756860_Rule-Based_Approach_for_Party-Based_Sentiment_Analysis_in_Legal_Opinion_Texts/links/5fc5ebeaa6fdcce952693061/largepreview.png},
  doi={10.1109/ICTer51097.2020.9325435}
}

@inproceedings{rajapaksha2021sigmalaw,
  title={{Sigmalaw PBSA-A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain}},
  author={Rajapaksha, Isanka and Mudalige, Chanika Ruchini and Karunarathna, Dilini and Silva, Nisansa de and Perera, Amal Shehan and Ratnayaka, Gathika},
  booktitle={International Conference on Database and Expert Systems Applications},
  pages={125--137},
  year={2021},
  organization={Springer},
  abstract={Legal information retrieval holds a significant importance to lawyers and legal professionals. Its significance has grown as a result of the vast and rapidly increasing amount of legal documents available via electronic means. Legal documents, which can be considered flat file databases, contain information that can be used in a variety of ways, including arguments, counter-arguments, justifications, and evidence. As a result, developing automated mechanisms for extracting important information from legal opinion texts can be regarded as an important step toward introducing artificial intelligence into the legal domain. Identifying advantageous or disadvantageous statements within these texts in relation to legal parties can be considered as a critical and time consuming task. This task is further complicated by the relevance of context in automatic legal information extraction. In this paper, we introduce a solution to predict sentiment value of sentences in legal documents in relation to its legal parties. The Proposed approach employs a fine-grained sentiment analysis (Aspect-Based Sentiment Analysis) technique to achieve this task. Sigmalaw PBSA is a novel deep learning-based model for ABSA which is specifically designed for legal opinion texts. We evaluate the Sigmalaw PBSA model and existing ABSA models on the SigmaLaw-ABSA dataset which consists of 2000 legal opinion texts fetched from a public online data base. Experiments show that our model outperforms the state-of-the-art models. We also conduct an ablation study to identify which methods are most effective for legal texts.},
  misc={https://bit.ly/2Vh109D,NLP:ML:LAW,https://goo.gl/iY6aTr},
  doi={10.1007/978-3-030-86472-9_12}
}

@article{rajapaksha2021sigmalawJ,
  title={{Sigmalaw PBSA-A Deep Learning Approach for Aspect-Based Sentiment Analysis in Legal Opinion Texts}},
  author={Rajapaksha, Isanka and Mudalige, Chanika Ruchini and Karunarathna, Dilini and Silva, Nisansa de and Ratnayaka, Gathika and Perera, Amal Shehan},
  journal={Journal of Data Intelligence},
  abstract={When lawyers and legal officers are working on a new legal case, they are supposed have properly studied prior cases similar to the current case, as the prior cases can provide valuable information which can have a direct impact on the outcomes of the current court case. Therefore, developing methodologies which are capable of automatically extracting information from legal opinion texts related to previous court cases can be considered as an important tool when it comes to the legal technology ecosystem. In this study, we focus on finding advantageous and disadvantageous facts or arguments in court cases, which is one of the most critical and time-consuming tasks in court case analysis. The Aspect-based Sentiment Analysis concept is used as the base of this study to perform legal information extraction. In this paper, we introduce a solution to predict sentiment value of sentences in legal documents in relation to its legal parties. The proposed approach employs a fine-grained sentiment analysis (Aspect-Based Sentiment Analysis) technique to achieve this task. Sigmalaw PBSA is a novel deep learning-based model for ABSA which is specifically designed for legal opinion texts. We evaluate the Sigmalaw PBSA model and existing ABSA models on the SigmaLaw-ABSA dataset which consists of 2000 legal opinion texts fetched from a public online data base. Experiments show that our model outperforms the state-of-the-art models. We also conduct an ablation study to identify which methods are most effective for legal texts.},
  volume={3},
  number={1},
  pages={101--115},
    misc={https://www.rintonpress.com/xjdi3/xjdi3-1/101-115.pdf,NLP:ML:LAW,https://goo.gl/iY6aTr},
    doi={10.26421/JDI3.1-1},
  year={2021}
}