TextFeatureSelection is a Python package providing feature selection for text tokens through filter method of feature selection and we can set a threshold to decide which words to be included. There are 4 methods for helping feature selection.

• Chi-square It measures the lack of independence between term(t) and class(c). It has a natural value of zero if t and c are independent. If it is higher, then term is dependent. It is not reliable for low-frequency terms
• Mutual information Rare terms will have a higher score than common terms. For multi-class categories, we will calculate MI value for all categories and will take the Max(MI) value across all categories at the word level.
• Proportional difference How close two numbers are from becoming equal. It helps find unigrams that occur mostly in one class of documents or the other.
• Information gain It gives discriminatory power of the word.

• target list object which has categories of labels. for more than one category, no need to dummy code and instead provide label encoded values as list object.
• input_doc_list List object which has text. each element of list is text corpus. No need to tokenize, as text will be tokenized in the module while processing. target and input_doc_list should have same length.
• stop_words Words for which you will not want to have metric values calculated. Default is blank
• metric_list List object which has the metric to be calculated. There are 4 metric which are being computed as 'MI','CHI','PD','IG'. you can specify one or more than one as a list object. Default is ['MI','CHI','PD','IG']. Chi-square(CHI), Mutual information(MI), Proportional difference(PD) and Information gain(IG) are 4 metric which are calculated for each tokenized word from the corpus to aid the user for feature selection.

from TextFeatureSelection import TextFeatureSelection #Multiclass classification problem input_doc_list=['i am very happy','i just had an awesome weekend','this is a very difficult terrain to trek. i wish i stayed back at home.','i just had lunch','Do you want chips?'] target=['Positive','Positive','Negative','Neutral','Neutral'] fsOBJ=TextFeatureSelection(target=target,input_doc_list=input_doc_list) result_df=fsOBJ.getScore() print(result_df) #Binary classification input_doc_list=['i am content with this location','i am having the time of my life','you cannot learn machine learning without linear algebra','i want to go to mars'] target=[1,1,0,1] fsOBJ=TextFeatureSelection(target=target,input_doc_list=input_doc_list) result_df=fsOBJ.getScore() print(result_df)

pip install TextFeatureSelection

• numpy
• pandas
• scikit-learn
• collections

• A Comparative Study on Feature Selection in Text Categorization by Yiming Yang and Jan O. Pedersen

• Entropy based feature selection for text categorization by Christine Largeron, Christophe Moulin, Mathias Géry

• Categorical Proportional Difference: A Feature Selection Method for Text Categorization by Mondelle Simeon, Robert J. Hilderman

• Feature Selection and Weighting Methods in Sentiment Analysis by Tim O`Keefe and Irena Koprinska