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

## Important: [use our PyPI package](https://pypi.org/project/NewsSentiment/) instead of this model on the Hub
The Huggingface Hub architecture currently [does not support](https://github.com/huggingface/transformers/issues/14785) 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](https://pypi.org/project/NewsSentiment/).

## Description

This model is the currently [best performing](https://aclanthology.org/2021.eacl-main.142.pdf) 
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`](https://pypi.org/project/NewsSentiment/) and 
in its original GitHub repository named [`NewsMTSC`](https://github.com/fhamborg/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](https://aclanthology.org/2021.eacl-main.142.pdf). The
dataset, the model, and its source code can be viewed in our [GitHub repository](https://github.com/fhamborg/NewsMTSC).

We recommend to use our [PyPI package](https://pypi.org/project/NewsSentiment/) for sentiment classification since the Huggingface Hub platform seems to [not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification.


# How to cite
If you use the dataset or model, please cite our [paper](https://www.aclweb.org/anthology/2021.eacl-main.142/) ([PDF](https://www.aclweb.org/anthology/2021.eacl-main.142.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},
}
```

{"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "sentiment-analysis", "sentiment-classification", "targeted-sentiment-classification", "target-depentent-sentiment-classification"], "datasets": "fhamborg/news_sentiment_newsmtsc"}
transformers pytorch roberta text-classification sentiment-analysis sentiment-classification targeted-sentiment-classification target-depentent-sentiment-classification en dataset:fhamborg/news_sentiment_newsmtsc license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us 


# 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):



# 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):
TAGS
#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us 
 # 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):
passage: TAGS
#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us 
# 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):