Emotional_model module
- Emotional_model.clean_text(text)
It takes a string as input, and returns a string with all the non-alphanumeric characters removed, and all the words converted to lowercase
- Parameters
text – The text to be cleaned
- Returns
A string
- Emotional_model.csv_update(csv_list, emotion_cat, train_data)
This function takes in a list of csv files, a list of emotion categories, and a training data set. It then uses the training data to train a classifier, and then uses the classifier to predict the emotion category of each row in the csv files. It then appends the emotion category to the csv files
- Parameters
csv_list – a list of csv files to be updated
emotion_cat – a list of emotions
train_data – the training data
- Returns
Nothing is being returned.
- Emotional_model.generate_train_test(file_name)
It takes a file name as input, reads the file, and returns the train and test data
- Parameters
file_name – The name of the file you want to read in.
- Returns
train_X, test_X, train_y, test_y
- Emotional_model.single_predict(clf_svm, test_str)
It takes a trained model and a string as input, and returns a prediction
- Parameters
clf_svm – the trained model
test_str – a string of text to be classified
- Returns
The prediction of the class of the test string.
- Emotional_model.train_test(train_data)
It takes in a dataframe, splits it into train and test sets, vectorizes the text, trains a linear SVM, and returns the trained model
- Parameters
train_data – the dataframe that contains the data to be trained on
- Returns
The trained model
- Emotional_model.train_vectorizer(train_X, test_X)
It takes in a list of strings (train_X) and a list of strings (test_X) and returns a list of vectors (train_X_vectors) and a list of vectors (test_X_vectors)
- Parameters
train_X – the training data
test_X – The test data
- Returns
The vectorizer is being returned.