@article{russell1980,
  title={A circumplex model of affect},
  author={Russell, James A},
  journal={Journal of personality and social psychology},
  volume={39},
  number={6},
  pages={1161},
  year={1980},
  publisher={American Psychological Association}
}

@inproceedings{conneau2020unsupervised,
  title={Unsupervised cross-lingual representation learning at scale},
  author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  pages={8440--8451},
  year={2020}
}

@misc{lee2026dimabsabuildingmultilingualmultidomain,
  title = {DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis},
  author = {Lung-Hao Lee and Liang-Chih Yu and Natalia Loukashevich and Ilseyar Alimova and Alexander Panchenko and Tzu-Mi Lin and Zhe-Yu Xu and Jian-Yu Zhou and Guangmin Zheng and Jin Wang and Sharanya Awasthi and Jonas Becker and Jan Philip Wahle and Terry Ruas and Shamsuddeen Hassan Muhammad and Saif M. Mohammad},
  year = {2026},
  eprint = {2601.23022},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url = {https://arxiv.org/abs/2601.23022}
}

@misc{becker2026dimstancemultilingualdatasetsdimensional,
  title = {DimStance: Multilingual Datasets for Dimensional Stance Analysis},
  author = {Jonas Becker and Liang-Chih Yu and Shamsuddeen Hassan Muhammad and Jan Philip Wahle and Terry Ruas and Idris Abdulmumin and Lung-Hao Lee and Nelson Odhiambo and Lilian Wanzare and Wen-Ni Liu and Tzu-Mi Lin and Zhe-Yu Xu and Ying-Lung Lin and Jin Wang and Maryam Ibrahim Mukhtar and Bela Gipp and Saif M. Mohammad},
  year = {2026},
  eprint = {2601.21483},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url = {https://arxiv.org/abs/2601.21483}
}

@misc{liu2019robertarobustlyoptimizedbert,
  title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
  author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
  year = {2019},
  eprint = {1907.11692},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url = {https://arxiv.org/abs/1907.11692}
}

@misc{wang2024tutorialpretrainfinetuneparadigmnatural,
  title = {A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing},
  author = {Yu Wang and Wen Qu},
  year = {2024},
  eprint = {2403.02504},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url = {https://arxiv.org/abs/2403.02504}
}

@article{liu2012sentiment,
  title={Sentiment analysis and opinion mining},
  author={Liu, Bing},
  year={2012},
  publisher={Morgan and Claypool San Rafael, CA}
}

@inproceedings{yu-etal-2026-semeval,
    title = {{S}em{E}val-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis ({D}im{ABSA})},
    author = {Yu, Liang-Chih and Becker, Jonas and Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Lee, Lung-Hao and Lin, Ying-Lung and Wang, Jin and Wahle, Jan Philip and Ruas, Terry and Panchenko, Alexander and Alimova, Ilseyar and Chang, Kai-Wei and Wanzare, Lilian and Odhiambo, Nelson and Gipp, Bela and Mohammad, Saif M.},
    year = 2026,
    month = jun,
    booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)},
    publisher = {Association for Computational Linguistics}
}

@misc{lee2026dimabsa,
    title={DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis},
    author={Lung-Hao Lee and Liang-Chih Yu and Natalia Loukashevich and Ilseyar Alimova and Alexander Panchenko and Tzu-Mi Lin and Zhe-Yu Xu and Jian-Yu Zhou and Guangmin Zheng and Jin Wang and Sharanya Awasthi and Jonas Becker and Jan Philip Wahle and Terry Ruas and Shamsuddeen Hassan Muhammad and Saif M. Mohammad},
    year={2026},
    eprint={2601.23022},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2601.23022},
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423/",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{sun2019utilizing,
  title={Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence},
  author={Sun, Chi and Huang, Luyao and Qiu, Xipeng},
  booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  pages={380--385},
  year={2019}
}

@article{rogers-etal-2020-primer,
    title = "A Primer in {BERT}ology: What We Know About How {BERT} Works",
    author = "Rogers, Anna  and
      Kovaleva, Olga  and
      Rumshisky, Anna",
    editor = "Johnson, Mark  and
      Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2020.tacl-1.54/",
    doi = "10.1162/tacl_a_00349",
    pages = "842--866",
    abstract = "Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research."
}