---
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 and an easy-to-run wrapper of the pre-trained model for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec).
This space containt an implementation of method described in "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph).

The proposed method suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple". The current model uses the "labeled-edge" graph encoding, and achieves the following results on the held-out set of the NoReC dataset:

| Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
|:----------------------------:|:----------:|:---------------------------:|
|     0.434                    |  0.541      |        0.926                |


In "Word Substitution with Masked Language Models as Data Augmentation for Sentiment Analysis", we analyzed data augmentation strategies for improving performance of the model. Using masked-language modeling (MLM), we augmented the sentences with MLM-substituted words inside, outside, or inside+outside the actual sentiment tuples. The results below show that augmentation may be improve the model performance. This space, however, runs the original model trained without augmentation.

|                | Augmentation rate | Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision |
|----------------|-------------------|------------------------------|-----------|-----------------------------|
| Baseline       | 0%               | 43.39                        | 54.13     | 92.59                       |
| Outside        | 59%              | **45.08**                    | 56.18     | 92.95                       |
| Inside         | 9%               | 43.38                        | 55.62     | 92.49                       |
| Inside+Outside | 27%              | 44.12                        | **56.44** | **93.19**               |



The model can be easily used for predicting sentiment tuples as follows: 

```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'}]}]
```