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https://github.com/gatenlp/stanceclassifier

Stance Classifier for the WeVerify project
https://github.com/gatenlp/stanceclassifier

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Stance Classifier for the WeVerify project

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README 

        
# StanceClassifier

Stance Classifier for the WeVerify project



This is a re-implementation of Aker et al. (2017) ["Simple Open Stance Classification for Rumour Analysis"](https://arxiv.org/pdf/1708.05286.pdf). We replaced the Bag-of-words and BROWN features with [GloVe embeddings](https://nlp.stanford.edu/projects/glove/) (twitter embeddings with 200 dimensions). 



## Configuration

1) Requirements:



        python3.7

        nltk

        numpy

        scipy

        sklearn

    

        Get Vader lexicon: 

        `python -m nltk.downloader vader_lexicon`



2) Clone this repository



3) Download the [resources](https://github.com/GateNLP/StanceClassifier/releases/download/v0.1/resources.tar.gz) required for feature extraction and extract it inside the main folder (`StanceClassifer`)



4) Download the trained model or models that you want to use - each model is provided as a `tar.gz` file which should be unpacked in this folder, e.g. `curl -L  | tar xvzf -`



 - [Ensemble model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-ensemble.tar.gz): an English-language feature-based ensemble model built with Logistic Regression, Random Forest and Multi-Layer Perceptron classifiers

 - [English BERT-based model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-bert-english.tar.gz): Monolingual English BERT-based model, i.e. fine-tuning of BERT for the rumour stance classification task, using threshold moving for imbalanced data treatment

 - [Multilingual BERT-based model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-bert-multi.tar.gz): Multilingual version of the BERT-based model, tuned on the same English data as the above but based on the [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) underlying model



## Usage



### Basic usage

```

python -m StanceClassifier -l  -s  -o  -c 

```

Supported model names for the `-c` option: `ens` (ensemble), `bert-english` (English BERT model), `bert-multilingual` (multilingual BERT model)



The output is a class:

 - 0.0 = support

 - 1.0 = deny

 - 2.0 = query

 - 3.0 = comment



and a vector with the probabilities returned for each class.



The folder `examples` contains examples of original tweets and replies:

 - original_old and reply_old are examples of the old JSON files (140 characters)

 - original_new and reply_new are examples of the new JSON files (280 characters)



### `StanceClassifer` class (StanceClassifier.stance_classifier.StanceClassifier)

This is the main class in this project. If you want to add this project as part of your own project, you should import this class. 



### Server usage

We have implemented TCP and HTTP servers. Server parameters are defined in the `configurations.txt` file.



To run the TCP server:

```

python Run_TCP_StanceClassifier_Server.py

```



Testing the TCP server:

```

python Test_Run_TCP_StanceClassifier_Server.py

```



The HTTP server uses a TCP server already running:

```

python Run_HTTP_StanceClassifier_Server.py

```



To test the HTTP server:

```

python Test_Run_HTTP_StanceClassifier_Server.py

```



In addition, the `docker` directory contains configuration to build a Docker image running a particular model of the classifier as an HTTP endpoint compliant with the [API specification](https://european-language-grid.readthedocs.io/en/release1.1.2/all/A2_API/LTInternalAPI.html) of the [European Language Grid](https://www.european-language-grid.eu).



### Training new models

To train new models, you can edit `train_model.py` (more support will be given in the future). To run:

```

python train_model.py

```