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https://github.com/prrao87/tweet-stance-prediction

Applying NLP transfer learning techniques to predict Tweet stance toward a topic
https://github.com/prrao87/tweet-stance-prediction

natural-language-processing nlp openai-gpt python text-classification transfer-learning transformers ulmfit

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Applying NLP transfer learning techniques to predict Tweet stance toward a topic

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README 

        
# Stance Classification of Tweets using Transfer Learning

Applying transfer learning (using existing neural network architectures) 

to perform stance classification of Tweets as per the [SemEval 2016 Stance Detection Task](http://alt.qcri.org/semeval2016/task6/).



[The methodology is described in detail in this Medium post](https://towardsdatascience.com/transfer-learning-in-nlp-for-tweet-stance-classification-8ab014da8dde) and compared (in detail) the transfer learning approaches used.



For subtask A, the goal is to classify Tweets in response to a particular topic into one of three classes: *Favor*, 

*Against* and *None*. The provided notebooks attempt this using a technique in deep learning called *transfer learning*.

While transfer learning has been ubiquitous throughout computer vision applications since the success of ImageNet, it is only 

since 2017-18 that significant progress has been made for transfer learning in NLP applications. There have been a string of 

interesting papers in 2018 that discuss the power of language models in natural language understanding and how they can be 

used to provide pre-trained representations of a language's syntax, which can be far more useful when training a neural 

network for previously unseen tasks.



## Analysis Notebooks



See the included Jupyter notebooks for the stance classification workflow using 

ULMFit and the OpenAI transformer.



**Method 1: ULMFiT**



[ulmfit.ipynb](https://github.com/prrao87/tweet-stance-prediction/blob/master/ulmfit.ipynb): (LSTM-based approach)



**Method 2: OpenAI Transformer** 



[transformer.ipynb](https://github.com/prrao87/tweet-stance-prediction/blob/master/transformer.ipynb): (Transformer-based approach)



### Module Installation



The below sections highlight the installation steps for each approach used. 

Python 3.6+ and PyTorch 1.0.0 is used for all the work shown.



Set up virtual environment:



    python3 -m venv venv

    source venv/bin/activate

    pip3 install -r requirements.txt



Once virtual environment has been set up, activate it for further development.



    source venv/bin/activate



## PyTorch requirements

Install the latest version of ```pytorch``` (1.0+) as shown below:



    pip3 install -r pytorch-requirements.txt



## ULMFit with the *fastai* framework



This utilizes the *fastai* framework (built on top of PyTorch) to perform

stance classification. 



The notebook ```ulmfit.ipynb``` uses **v1** of ```fastai```, which has been 

refactored for efficiency and updated to move forward with future PyTorch versions (1.0+).



Install ```fastai``` as shown below:



    pip3 install fastai



## spaCy language model



For tokenization, ```fastai``` uses the SpaCy library's English language model. This has

to be downloaded manually:



    python3 -m spacy download en 



## Evaluation



To evaluate the F1 score as per the SemEval 2016 Task 6 guidelines, use the *perl* 

script given in ```data/eval/``` as shown:



    perl eval.pl -u



    ---------------------------

    Usage:

    perl eval.pl goldFile guessFile



    goldFile:  file containing gold standards;

    guessFile: file containing your prediction.



    These two files have the same format:

    IDTargetTweetStance

    Only stance labels may be different between them!

    ---------------------------