Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain (2020.coling-main)
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| Challenge: | Existing datasets in automotive domain cover only three languages due to high cost of human annotation. |
| Approach: | They build large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. |
| Outcome: | The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. |
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| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
| Approach: | They propose a metric to facilitate the evaluation of aspect extraction with generative models. |
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M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
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Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)
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| Challenge: | Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce. |
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A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language . there is no standard Japanese dataset available for ABSA task in the language - a paper by cnn. |
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DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis (2026.acl-long)
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Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad
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Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
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Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks (2024.lrec-main)
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| Challenge: | 3.1K reviews are manually annotated for aspect-based sentiment analysis (ABSA) ABSA is a fine-grained task that aims to identify the sentiment associated with each aspect or characteristic of a text. |
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A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
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| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
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A Large Multilingual and Multi-domain Dataset for Recommender Systems (L18-1)
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| Challenge: | Existing algorithms for recommending items are limited and focused on specific domains. |
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MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification (L18-1)
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| Challenge: | sentiment analysis research has focused on unsupervised or semi-supervised approaches, but these still require a large number of resources and do not reach the performance of supervised approaches. |
| Approach: | They propose two datasets for supervised aspect-level sentiment analysis in Basque and Catalan. |
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