fhamborg/roberta-targeted-sentiment-classification-newsarticles

NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles

Important: use our PyPI package instead of this model on the Hub

The Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use PyPI package NewsSentiment.

Description

This model is the currently best performing targeted sentiment classifier for news articles. In contrast to regular sentiment classification, targeted sentiment classification allows you to provide a target in a sentence. Only for this target, the sentiment is then predicted. This is more reliable in many cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."

This model is also available as an easy-to-use PyPI package named NewsSentiment and in its original GitHub repository named NewsMTSC, where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each labeled and agreed upon by at least 5 annotators) can be found in our EACL paper. The dataset, the model, and its source code can be viewed in our GitHub repository.

We recommend to use our PyPI package for sentiment classification since the Huggingface Hub platform seems to not support target-dependent sentiment classification.

How to cite

If you use the dataset or model, please cite our paper (PDF):

@InProceedings{Hamborg2021b,
  author    = {Hamborg, Felix and Donnay, Karsten},
  title     = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
  year      = {2021},
  month     = {Apr.},
  location  = {Virtual Event},
}