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ANN_NO_POLARITY_MULTICLASS.ipynb
ANN_NO_POLARITY_MULTICLASS.ipynb
 
 
ANN_POLARITY_MULTICLASS.ipynb
ANN_POLARITY_MULTICLASS.ipynb
 
 
ANNbiclassification.ipynb
ANNbiclassification.ipynb
 
 
README.md
README.md
 
 
SVM(BICLASSIFICATION).ipynb
SVM(BICLASSIFICATION).ipynb
 
 
SVM(MULTICLASSIFICATION).ipynb
SVM(MULTICLASSIFICATION).ipynb
 
 
amazon_mp3_SVM(MULTICLASSIFICATION).ipynb
amazon_mp3_SVM(MULTICLASSIFICATION).ipynb
 
 
amazon_mp3_dummy.txt
amazon_mp3_dummy.txt
 
 
amazon_mp3_txt_to_csv.ipynb
amazon_mp3_txt_to_csv.ipynb
 
 
equlibrium_sampling_ANN_POLARITY_MULTICLASS.ipynb
equlibrium_sampling_ANN_POLARITY_MULTICLASS.ipynb
 
 
sentimentONRATINGS.ipynb
sentimentONRATINGS.ipynb
 
 
sentimentONREVIEWS.ipynb
sentimentONREVIEWS.ipynb
 
 
train-processed.csv
train-processed.csv
 
 

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sentiment_analysis

using svm, binary classification, text reviews using svm , multi classification using lexicon+machine learning approach --> used feedforward 2-layer ann along with polarity scores of each word in vocabulary using under sampling(SMOTE) to reduce non-uniformity of data distribution dataset used #amazon-mp3 dataset #"the vinci code " book reviews dataset #"amazon products" dataset ##"sorry for the late upload"##

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using svm, binary classification, text reviews

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