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
Last synced: about 1 month ago
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Applying NLP transfer learning techniques to predict Tweet stance toward a topic
- Host: GitHub
- URL: https://github.com/prrao87/tweet-stance-prediction
- Owner: prrao87
- License: mit
- Created: 2018-12-24T07:00:10.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-10T20:46:53.000Z (over 6 years ago)
- Last Synced: 2025-04-06T19:43:13.425Z (about 1 month ago)
- Topics: natural-language-processing, nlp, openai-gpt, python, text-classification, transfer-learning, transformers, ulmfit
- Language: Jupyter Notebook
- Homepage:
- Size: 1.83 MB
- Stars: 107
- Watchers: 5
- Forks: 57
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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!
---------------------------