MOOC-text-Prioritization

Classification of MOOC posts into urgent and non-urgent using semi-supervised Learning algorithms and word embeddings If you want to read more about this work find a detailed report here: https://repository.lib.ncsu.edu/bitstream/handle/1840.20/36642/etd.pdf?sequence=1&isAllowed=y

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
README.md		README.md
Semi_EM_NB.py		Semi_EM_NB.py
compare_labels_probabilities.py		compare_labels_probabilities.py
correlations.py		correlations.py
cross_domain_classification.py		cross_domain_classification.py
cross_domain_java.py		cross_domain_java.py
data_preprocessing_1.py		data_preprocessing_1.py
data_preprocessing_2.py		data_preprocessing_2.py
data_preprocessing_3.py		data_preprocessing_3.py
expectation_maximization.py		expectation_maximization.py
feature_model.py		feature_model.py
feature_selection.py		feature_selection.py
filtering_model.py		filtering_model.py
k_alpha.py		k_alpha.py
main_file.py		main_file.py
metadata.py		metadata.py
one_class_model.py		one_class_model.py
one_class_svm_validation.py		one_class_svm_validation.py
question_detector.py		question_detector.py
semi_supervised_classification.py		semi_supervised_classification.py
supervised_models.py		supervised_models.py
validation.py		validation.py
visualization.py		visualization.py
w2v_model		w2v_model
word_feature_extraction.py		word_feature_extraction.py
word_to_vec_vectorizer.py		word_to_vec_vectorizer.py

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