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title: Sentiment Analysis | |
emoji: 🤔 | |
colorFrom: purple | |
colorTo: yellow | |
sdk: gradio | |
sdk_version: 3.1.7 | |
app_file: app.py | |
pinned: false | |
This space provides a gradio demo of a [pretrained model](https://huggingface.co/ltg/ssa-perin) (with an easy-to-run wrapper) for structured sentiment analysis (SSA) of Norwegian text, trained on the [NoReC_fine](https://github.com/ltgoslo/norec_fine) dataset. It implements a method described in the paper [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by Samuel et al. 2022. | |
The model will attempt to identify the following components for a given sentence it deems to be sentiment-bearing: _source expressions_ (the opinion holder), _target expressions_ (what the opinion is directed towards), _polar expressions_ (the part of the text indicating that an opinion is expressed), and finally the _polarity_ (positive or negative). | |
See the code below for an example of how you can use the model yourself for predicting such sentiment tuples (along with character offsets in the text): | |
```python | |
>>> import model_wrapper | |
>>> model = model_wrapper.PredictionModel() | |
>>> model.predict(['vi liker svart kaffe']) | |
[{'sent_id': '0', | |
'text': 'vi liker svart kaffe', | |
'opinions': [{'Source': [['vi'], ['0:2']], | |
'Target': [['svart', 'kaffe'], ['9:14', '15:20']], | |
'Polar_expression': [['liker'], ['3:8']], | |
'Polarity': 'Positive'}]}] | |
``` | |
To download the model and find more in-depth documentation, please see (https://huggingface.co/ltg/ssa-perin)[https://huggingface.co/ltg/ssa-perin] | |