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CRF Predictive model to classify conversations into 12 different labels with sentiment visualization of each conversation

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ConversationLabelling

Steps: 1.Pre processing (preProcess/preProcess.py) --change the directory path of train_path (give the path of your trainData folder) --change the directory path of data_path (give the path of your csv file) --Run preProcess.py

After this step all the conversations goes into trainData Folder along with some of the features.

  1. Copy Data Copy[20 % or 10% ] whatever files you want to test on to the testData folder from trainData folder.

3.Run basic_crf.py In basic_crf.py test_accuracy()[sklearn] and test_for_accuracy()[python crf suite ] are the only used fuctions right now.

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CRF Predictive model to classify conversations into 12 different labels with sentiment visualization of each conversation

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