The following code is meant to train NLP models on a corpus of tweets. Under are some instructions on how to use it in details but let's first describe the most useful work-arounds.
Every python script that is meant to be run by the user uses the library argparse, that allows you to feed the program the argument it needs to work, when you don't know what arguments to feed to a program, don't worry, and ask for help
python my_script.py -h
It will show you the list of argument needed, with some explanation.
For simplicity & educational purpose, some shell scripts calling the python scripts with the right arguments are available, you can run them with.
shell my_script.sh
However, you may run into trouble if you call these scripts without creating the dictionnary they use. The directories I mention in this script are Data/ and Models/ . They are suppose to contain respectively the tweets and several subsets of them, and the machine learning models as well as the CountVectorizer objects. So before starting, you should call:
mkdir Models Data
Ok now let's dive into the subject. Before experimenting with a model you need to train it.
Files concerned: - brexit_tweets_all.jsonl ( you have to download it ) - config.json - get_labeled_data.py
First, you need to build the training data by selecting which hashtags are "for brexit" and which are "against brexit" this is done in the config.json file.
Now you can extract the number of tweets you want by calling the script get_labeled_data.py. It takes as argument the input and output files, the configuration file and the number of tweets to extract. Be aware that if they aren't enough tweets satisfying the requirements in the initial file, it won't work.
python get_labeled_data.py --input brexit_tweets_all.jsonl --output Data/training.jsonl --limit 10000 --config config.json
This both filters tweets of the wanted hashtags and annotate every tweet by adding a sentiment key, equal to 0 ( against brexit ) or 1 ( for brexit ).
Files concerned: - create_tokenizer.py - create_tokenizer.sh
In order to train a model on a corpus of text you need to be able to turn this corpus into a matrix containing one example per line. This is what the CountVectorizer does. This object will be trained on a corpus and will link an index to every word (also lists of words optionnaly ) under certain condition.
Before creating this object, you can tune it's parameter by modifying the source python file ( ye that's dirty ).
Now that we have a training set and an object to turn sentences into vectors we can train any machine learning model that we want. The one currently available are SVM, Naive Bayes, RandomForest and Multi Layer Perceptron.
To train a model, run one of this file like that
python model_file.py --input Data/training.jsonl --model
Models/my_model.p --tokenizer Models/my_tokenizer.p
The script will print the final accuracy on the test set and will save the model into the given file.
Files concerned: - evaluate.py
This file only contains one function that gives some more precise metrics on a model. It is called at the end of training the multi layer perceptron.
Files concerned: - interact_with_model.py - label_new.py - label_new.sh
Important thing to note: If you want your model to work correctly, you have to use the same CountVectorizer object it has been trained with, so to save a model, save both the CountVectorizer object and the model.
Open the interact_with_model.py script in interpretation mode to use the function predict("my text").
python interact_with_model.py --model Models/my_model.p --tokenizer Models/my_tokenizer.p
The file label_new.py can be used to label a new unseen collection of tweets, you can then judge yourself if the tweets have been annotated correctly.