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dc.contributor.authorGad, Samah
dc.date.accessioned2014-02-24T17:52:02Z
dc.date.available2014-02-24T17:52:02Z
dc.date.issued2014-02-24
dc.identifier.urihttp://hdl.handle.net/10919/25528
dc.descriptionThis software was developed as a part of research in applied algorithmic techniques to identify qualitative features and information flow in large text-based data sets. This folder contains 15 files: README.txt results_file.csv Tone Classifier Running Instructions.txt.txt Tone_Classifier_Train.py Tone_Classifier.py ToneClassifier.pkl ToneClassifier.pkl_01.npy ToneClassifier.pkl_02.npy ToneClassifier.pkl_03.npy ToneTestingSet.csv ToneTrainingSet.csv Vectorizer_tfidf.pkl Vectorizer_tfidf.pkl_01.npy Vectorizer_tfidf.pkl_02.npy Vectorizer_tfidf.pkl_03.npy THIS DATA SET IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANT ABILITY AND FITNESS FOR A PARTICULAR PURPOSE.en_US
dc.description.abstractThe goal of tone analysis is to identify tone from text. We focused on the following tones: alarmist, warning, reassuring, and explanatory. To detect tones from text automatically, we used a supervised machine learning approach. This is a classic text classification problem, and a usual practice in approaching such problems is to first examine text chunks using a Multinomial Naïve Bayes classifier (based on the bag-of-words model). The classifier is based on Bayes’s theorem with a feature model that is conditionally independent of the tone. The classifier is first trained using the features extracted from manually tagged text. After training, the classifier predicts tones for newly extracted, previously unseen, text.en_US
dc.description.sponsorshipNational Endowment for the Humanitiesen_US
dc.description.sponsorshipSocial Science and Humanities Research Council of Canadaen_US
dc.description.sponsorshipVirginia Techen_US
dc.description.sponsorshipUniversity of Torontoen_US
dc.language.isoen_USen_US
dc.titleTone classifieren_US
dc.typeSoftwareen_US


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