def main():
# LOAD TRAIN SET
dataset_train = Dataset.DatasetReview()
dataset_train.load_review_from_csv(train_set)
# LOAD TEST SET
dataset_test = Dataset.DatasetReview()
dataset_test.load_review_from_csv(test_set)
fe_tfidf = TfidfFeatureExtractor(size=500)
fe_w2v = WordEmbeddingFeatureExtractor(infile=w2v_vec_path, binary=False, dimen=500)
fe_sswe_w2v = WordEmbeddingFeatureExtractor(infile=sswe_w2v, binary=False, dimen=500, sswe=1)
fe_sswe = SennaFeatureExtractor(infile=sswe_senna_vectors, vocabfile=sswe_senna_vocabs, dimen=500)
feature_extractors = [fe_tfidf, fe_w2v, fe_sswe_w2v, fe_sswe]
ev = Evaluator()
print "\n**** CROSS VALIDATION EVALUATION (CORPUS: DATASET) ****\n"
model = Classifier(models="nn")
kfold = KFold(n_splits=10)
ev.eval_with_cross_validation(model, feature_extractors=feature_extractors,
training_set=dataset_train, num_fold=10, cv=kfold)
model = Classifier(models="nn")
ev.create_evaluation_result(model, feature_extractors=feature_extractors,
training_set=dataset_train, num_fold=10, cv=kfold)
print "\n**** TEST SET EVALUATION (CORPUS: DATASET) ****\n"
model = Classifier(models="nn")
ev.eval_with_test_set(model, feature_extractors=feature_extractors,
training_set=dataset_train,
test_set=dataset_test)
def main(train_set, output_path):
# LOAD TRAIN SET
dataset_train = Dataset.DatasetReview()
dataset_train.load_review_from_csv(train_set)
# LOAD TEST SET
# dataset_test = Dataset.DatasetReview()
# dataset_test.load_review_from_csv(test_set)
# preprocessor = DatasetPreprocessor()
# dataset = preprocessor.fold_cases_d(dataset)
# dataset = preprocessor.remove_punctuations_d(dataset)
# dataset = preprocessor.convert_numbers_d(dataset)
# dataset.export_only_contents("../Test/dataset.txt")
# fe = BagFeatureExtractor(dataset.get_contents())
# fe.build()
# fe.save_vocab("../Test/vocab.txt")
# dataset.export_formatted_dataset("formatted_dataset_wow.tsv")
if not os.path.exists(output_path):
os.makedirs(output_path)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(), dimen=300)
fe.save_model_to_file(output_path + "vectors_full_300.txt",
vocabfile=output_path + "vocab_full_300.txt",
binary=False)
def main(infile):
# LOAD TRAIN SET
dataset_train = Dataset.DatasetReview()
dataset_train.load_review_from_csv(infile)
dataset_train.export_only_contents("sentences_new.txt")
# fe = BagFeatureExtractor(dataset.get_contents())
# fe.build()
# fe.save_vocab("../Test/vocab.txt")
dataset_train.export_formatted_dataset("formatted_dataset_wow.tsv")
def main(infile):
# LOAD TRAIN SET
dataset_train = Dataset.DatasetReview()
dataset_train.load_review_from_csv(train_set)
# LOAD TEST SET
dataset_test = Dataset.DatasetReview()
dataset_test.load_review_from_csv(test_set)
# preprocessor = DatasetPreprocessor()
# dataset = preprocessor.fold_cases_d(dataset)
# dataset = preprocessor.remove_punctuations_d(dataset)
# dataset = preprocessor.convert_numbers_d(dataset)
# dataset.export_only_contents("../Test/dataset.txt")
# fe = BagFeatureExtractor(dataset.get_contents())
# fe.build()
# fe.save_vocab("../Test/vocab.txt")
# dataset.export_formatted_dataset("formatted_dataset_wow.tsv")
print "\n**** CROSS VALIDATION EVALUATION (CORPUS: WIKIPEDIA) ****\n"
fe = BagFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_cross_validation(classifier, fe, dataset_train)
fe = TfidfFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_cross_validation(classifier, fe, dataset_train)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile=wiki_w2v_model,
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full.txt", vocabfile="vocab_full.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_cross_validation(classifier, fe, dataset_train)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile="vectors_full_wow.txt",
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full.txt", vocabfile="vocab_full.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_cross_validation(classifier, fe, dataset_train)
fe = SennaFeatureExtractor(dataset_train.get_contents(),
infile="../senna_vectors.txt",
vocabfile="../senna_vocab.txt")
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_cross_validation(classifier, fe, dataset_train)
print "\n**** TRAINING SET EVALUATION (CORPUS: WIKIPEDIA) ****\n"
fe = BagFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_training_set(classifier, fe, dataset_train)
fe = TfidfFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_training_set(classifier, fe, dataset_train)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile=wiki_w2v_model,
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full_wow.txt", vocabfile="vocab_full_wow.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_training_set(classifier, fe, dataset_train)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile="vectors_full_wow.txt",
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full.txt", vocabfile="vocab_full.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_training_set(classifier, fe, dataset_train)
fe = SennaFeatureExtractor(dataset_train.get_contents(),
infile="../senna_vectors.txt",
vocabfile="../senna_vocab.txt")
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_training_set(classifier, fe, dataset_train)
print "TEST SET EVALUATION (CORPUS: WIKIPEDIA)"
fe = BagFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_test_set(classifier, fe, dataset_train, dataset_test)
fe = TfidfFeatureExtractor(dataset_train.get_contents())
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_test_set(classifier, fe, dataset_train, dataset_test)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile=wiki_w2v_model,
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full_wow.txt", vocabfile="vocab_full_wow.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_test_set(classifier, fe, dataset_train, dataset_test)
fe = WordEmbeddingFeatureExtractor(dataset_train.get_contents(),
infile="vectors_full_wow.txt",
binary=False,
dimen=200)
# fe.save_model_to_file("vectors_full.txt", vocabfile="vocab_full.txt", binary=False)
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_test_set(classifier, fe, dataset_train, dataset_test)
fe = SennaFeatureExtractor(dataset.get_contents(),
infile="../senna_vectors.txt",
vocabfile="../senna_vocab.txt")
classifier = Classifier(models="svm")
ev = Evaluator()
ev.eval_with_test_set(classifier, fe, dataset_train, dataset_test)
def main():
""" Sentiment Specific Embedding for twitter classification """
embeddings_size = 50 # Embedding size for SSWE model
vocab_file = "Embedding/features/semeval_vocabs_200.txt" # path to the vocabulary file
vector_file = "Embedding/features/semeval_vectors_200.txt" # path to the vector file
stopwordsfile = "preprocess/stopwords.txt"
""" Sentiment-Specific Word Embedding (SSWE) """
if True:
# Load dataset
data_train = 'dataset/training1600000.csv' # training data set file path
pre_data_train = 'dataset/preprocessed_dataset1600000.csv' # file to save dataset after cleaning
if True:
print("\n **** Dataset cleaning ****")
tweets_prepocess(data_train, pre_data_train, stopwordsfile)
if True:
print("\n **** SSWE model Trainig ****")
train_model = None # path to the file contains the trained model if it is already exist
save_model = "Embedding/models/SSWE_model_1600000_200" # path to the file where model will be saved
sswe = create_sswe_model(pre_data_train, vocab_file, vector_file,
train_model, save_model, embeddings_size)
sswe_trainer(sswe)
""" Embedding visualisation and Similarity computing """
if True:
visualiser = Visualiser(
sizeOfEmbedding=embeddings_size,
VocabsFname=vocab_file,
VectorsFname=vector_file,
WVFilename="Visualisation/data/w2vformat.txt",
visualizerHTMLfilename="Visualisation/data/embedding.html")
visualiser.visualize()
""" Twitter Sentiment Classification """
if True:
# Data pre-processing
print("\n **** Training data cleaning ****")
pre_processing_train = "dataset/preprocessed_semeval_traindataset.csv"
# tweets_prepocess(train_set, pre_processing_train, stopwordsfile)
print("\n **** Test data cleaning ****")
pre_processing_test = "dataset/preprocessed_semeval_testdataset.csv"
# tweets_prepocess(test_set, pre_processing_test, stopwordsfile)
# LOAD TRAIN SET
dataset_train = Dataset.DatasetReview()
dataset_train.load_review_from_csv(pre_processing_train)
# LOAD TEST SET
dataset_test = Dataset.DatasetReview()
dataset_test.load_review_from_csv(pre_processing_test)
################################### Neural Nets classifier ###########################
# Extract Features
tweet2v = get_sswe_features(vocab_file, vector_file)
# Extract samples and labels
x_train, y_train = split_data(dataset_train)
x_test, y_test = split_data(dataset_train)
tfidf = build_tfidf(x_train)
train_vecs_sswe = np.concatenate([
buildWordVector(z.split(), embeddings_size, tweet2v, tfidf)
for z in tqdm(map(lambda x: x, x_train))
])
train_vecs_sswe = scale(train_vecs_sswe)
test_vecs_sswe = np.concatenate([
buildWordVector(z.split(), embeddings_size, tweet2v, tfidf)
for z in tqdm(map(lambda x: x, x_test))
])
test_vecs_sswe = scale(test_vecs_sswe)
# neural network model
neuralnets = NeuralNets(input_size=embeddings_size,
x_train=train_vecs_sswe,
y_train=y_train,
epochs=450,
batch_size=32,
x_test=test_vecs_sswe,
y_test=y_test)
neuralnets.train_neural_nets()
##########################################################################################
########
######## Classical classifiers with sklearn
########
##########################################################################################
print("\n**** CROSS VALIDATION EVALUATION (CORPUS: SemEval) ****\n")
fe_sswe = SennaFeatureExtractor(infile=vector_file,
vocabfile=vocab_file,
dimen=embeddings_size)
feature_extractors = [fe_sswe]
ev = Evaluator()
################################# SVM ###################################################
print("\n**** CROSS VALIDATION EVALUATION (CORPUS: SemEval) ****\n")
model = Classifier(models="svm")
kfold = KFold(n_splits=10)
ev.eval_with_cross_validation(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
ev.create_evaluation_result(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
print("\n**** TEST SET EVALUATION (CORPUS: SemEval) ****\n")
ev.eval_with_test_set(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
test_set=dataset_test)
################################### Naive bayes ##########################################
print("\n**** CROSS VALIDATION EVALUATION (CORPUS: SemEval) ****\n")
model = Classifier(models="multinomial")
kfold = KFold(n_splits=10)
ev.eval_with_cross_validation(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
ev.create_evaluation_result(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
print("\n**** TEST SET EVALUATION (CORPUS: DATASET) ****\n")
ev.eval_with_test_set(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
test_set=dataset_test)
######################################### RandomForestClassifier #######################
print("\n**** CROSS VALIDATION EVALUATION (CORPUS: SemEval) ****\n")
model = Classifier(models="rfc")
kfold = KFold(n_splits=10)
ev.eval_with_cross_validation(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
ev.create_evaluation_result(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
print("\n**** TEST SET EVALUATION (CORPUS: SemEval) ****\n")
ev.eval_with_test_set(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
test_set=dataset_test)
######################################### MLPClassifier #######################
print("\n**** CROSS VALIDATION EVALUATION (CORPUS: SemEval) ****\n")
model = Classifier(models="nn")
kfold = KFold(n_splits=10)
ev.eval_with_cross_validation(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
ev.create_evaluation_result(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
num_fold=10,
cv=kfold)
print("\n**** TEST SET EVALUATION (CORPUS: SemEval) ****\n")
ev.eval_with_test_set(model,
feature_extractors=feature_extractors,
training_set=dataset_train,
test_set=dataset_test)
