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Projeto de Pós-doutorado "Identificação Automática de Ironias em textos em Português"

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1_features_gabriela_obtencao-Corpus2 (400).ipynb
1_features_gabriela_obtencao-Corpus2 (400).ipynb
 
 
1_features_gabriela_obtencao.ipynb
1_features_gabriela_obtencao.ipynb
 
 
1_features_gabriela_obtencao_com_LIWC.ipynb
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1a_features_gabriela_pca.ipynb
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1c_features_gabriela_liwc_pca.ipynb
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2aX_features_gabriela_machine_learning_10perc-XGBoost.ipynb
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2a_features_gabriela_machine_learning_10k_fold_cross_validation-5-features.ipynb
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2a_features_gabriela_machine_learning_10perc-200-liwc.ipynb
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2a_features_gabriela_machine_learning_10perc-200.ipynb
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2a_features_gabriela_machine_learning_10perc-4features-200-chisqaured.ipynb
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2a_features_gabriela_machine_learning_10perc-4features-200-gabriela.ipynb
2a_features_gabriela_machine_learning_10perc-4features-200-gabriela.ipynb
 
 
2a_features_gabriela_machine_learning_10perc.ipynb
2a_features_gabriela_machine_learning_10perc.ipynb
 
 
2a_features_gabriela_machine_learning_20perc-4features-chisquared.ipynb
2a_features_gabriela_machine_learning_20perc-4features-chisquared.ipynb
 
 
2a_features_gabriela_machine_learning_20perc-4features-gabriela.ipynb
2a_features_gabriela_machine_learning_20perc-4features-gabriela.ipynb
 
 
2ab_features_gabriela_liwc_machine_learning_10perc.ipynb
2ab_features_gabriela_liwc_machine_learning_10perc.ipynb
 
 
2ab_features_gabriela_liwc_machine_learning_20perc.ipynb
2ab_features_gabriela_liwc_machine_learning_20perc.ipynb
 
 
2b_features_gabriela_machine_learning_20perc.ipynb
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2c_features_gabriela_liwc_machine_learning_10_fold_cross_validation.ipynb
2c_features_gabriela_liwc_machine_learning_10_fold_cross_validation.ipynb
 
 
2c_features_gabriela_machine_learning_10k_fold_cross_validation.ipynb
2c_features_gabriela_machine_learning_10k_fold_cross_validation.ipynb
 
 
3_pre_processamento_sentencas-Corpus2 (400).ipynb
3_pre_processamento_sentencas-Corpus2 (400).ipynb
 
 
3_pre_processamento_sentencas.ipynb
3_pre_processamento_sentencas.ipynb
 
 
4a_bow_machine_learning_10perc-Corpus2 (400).ipynb
4a_bow_machine_learning_10perc-Corpus2 (400).ipynb
 
 
4a_bow_machine_learning_10perc.ipynb
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4a_bow_machine_learning_10perc_with_xgboost.ipynb
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4a_features_keras_10perc.ipynb
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4b_bow_machine_learning_20perc.ipynb
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4c_bow_machine_learning_10k_fold_cross_validation.ipynb
4c_bow_machine_learning_10k_fold_cross_validation.ipynb
 
 
5a_tfidf_machine_learning_10perc-Corpus2 (400).ipynb
5a_tfidf_machine_learning_10perc-Corpus2 (400).ipynb
 
 
5a_tfidf_machine_learning_10perc.ipynb
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5a_tfidf_machine_learning_10perc_wrong.ipynb
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5b_tfidf_machine_learning_20perc.ipynb
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5c_tfidf_machine_learning_10k_fold_cross_validation.ipynb
5c_tfidf_machine_learning_10k_fold_cross_validation.ipynb
 
 
6_10_features_gabriela_liwc_machine_learning_10_fold_cv-final.ipynb
6_10_features_gabriela_liwc_machine_learning_10_fold_cv-final.ipynb
 
 
6_10_features_gabriela_liwc_machine_learning_10_fold_cv.ipynb
6_10_features_gabriela_liwc_machine_learning_10_fold_cv.ipynb
 
 
6_10_features_gabriela_machine_learning_10_fold_cv.ipynb
6_10_features_gabriela_machine_learning_10_fold_cv.ipynb
 
 
6_4_features_gabriela_chi_squared_machine_learning_10_fold_cv.ipynb
6_4_features_gabriela_chi_squared_machine_learning_10_fold_cv.ipynb
 
 
6_4_features_gabriela_machine_learning_10_fold_cv.ipynb
6_4_features_gabriela_machine_learning_10_fold_cv.ipynb
 
 
6_bow_machine_learning_10_fold_cv_corpus2.ipynb
6_bow_machine_learning_10_fold_cv_corpus2.ipynb
 
 
6_tfidf_machine_learning_10_fold_cv_corpus2-final.ipynb
6_tfidf_machine_learning_10_fold_cv_corpus2-final.ipynb
 
 
6_tfidf_machine_learning_10_fold_cv_corpus2.ipynb
6_tfidf_machine_learning_10_fold_cv_corpus2.ipynb
 
 
7_5_chi_squared_10_cross_validation.ipynb
7_5_chi_squared_10_cross_validation.ipynb
 
 
7_5_features_gabriela_10_fold_cross_validation.ipynb
7_5_features_gabriela_10_fold_cross_validation.ipynb
 
 
7_all_features_10_fold_cross_validation.ipynb
7_all_features_10_fold_cross_validation.ipynb
 
 
7_bow_10_cross_validation.ipynb
7_bow_10_cross_validation.ipynb
 
 
7_liwc_10_cross_validation.ipynb
7_liwc_10_cross_validation.ipynb
 
 
7_tfidf_10_cross_validation.ipynb
7_tfidf_10_cross_validation.ipynb
 
 
README.txt
README.txt
 
 
corpus.csv
corpus.csv
 
 
dataset_features.csv
dataset_features.csv
 
 
dataset_features_liwc.csv
dataset_features_liwc.csv
 
 
dataset_full.csv
dataset_full.csv
 
 
dataset_full_400.csv
dataset_full_400.csv
 
 
dataset_sentencas_processadas.csv
dataset_sentencas_processadas.csv
 
 
dataset_sentencas_processadas_400.csv
dataset_sentencas_processadas_400.csv
 
 
liwc2.txt
liwc2.txt
 
 
tamanho200_3.csv
tamanho200_3.csv
 
 
word2vec_gensim_gabriela.ipynb
word2vec_gensim_gabriela.ipynb
 
 

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Projeto desenvolvido por Prof. Dr. Henrique Dezani no programa de pós-doutorado em Ciência da Computação pela Universidade Federal de São Carlos (UFSCar), sob a supervisão da profa. Dra. Helena Caseli.

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Projeto de Pós-doutorado "Identificação Automática de Ironias em textos em Português"

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