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Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification (AAAI'19)
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Data for our AAAI'19 paper "Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification".

2019/02/24 Update: Data resource has been released now! More details will be updated.


This dataset acts as highly beneficial source domains to improve the learning of more fine-grained aspect-term level (AT) sentiment analysis. The dataset has three characteristics:

Large-scale: 100k for each domain

Multi-domain: Restaurant, Hotel, Beautyspa

Aspect-category (AC): Coarse-grained asepct

Even with a simple attention-based model for the AT task, our method can achieve the STOA performances by leveraging the knowledge distilled from the AC task.

Data format

Each instance behaves as the format below:

inst1: ID1/sentence1/aspect1/label1

inst2: ID1/sentence1/aspect2/label2

inst3: ID1/sentence1/aspect3/label3


The sentence containing multiple aspects are arranged together.

Label Mapping

positive: 1 neutral: 0 negative: -1


If the data is useful for your research, please be kindly to give us stars and cite our paper as follows:

  title={Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification},
  author={Li, Zheng and Wei, Ying and Zhang, Yu and Zhang, Xiang and Li, Xin and Yang, Qiang},
  journal={arXiv preprint arXiv:1811.10999},
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