Predicting whether the tweet is Favored or Against using TF-IDF and Global Vector Models.

Running Instructions

Prerequisites:

1. Python 2.7

2. Pandas

3. Numpy

4. Sci-kitlearn(sklearn)

5. Natural Language Processing Toolkit (nltk)

6. Natural Language Processing Toolkit Data(words,punkt)

   	To download the above package the following commands need to be executed from python shell
   	Import nltk
   		nltk.download(‘punkt’)
   		nltk.download(‘words’)
   

7. Download Global Vector prebuilt model from https://nlp.stanford.edu/data/glove.twitter.27B.zip

Running Instructions

1. Unzip the folder downloaded from above url and place all the files in code folder provided with the submission.

2. To run the program type the below command after navigating to code foler

   python biasDetectionMain.py --trainingfile training.txt --testfile test-gold.txt --choice g --glovefile 			
   glove.twitter.27B.25d.txt --evaluation k --classifier svc
   
   parameters need to be supplied :
   a)	–trainingfile : path to training.txt file in code folder
   b)	–testfile :path to test-gold.txt file code folder
   c)	–glovefile: path to global vector file.  We can give file with 25 dimensions or 50 dimensions or 100 			      			
   		dimensions or 200 dimensions 
   		eg for 50 dimensions
   		--glovefile glove.twitter.27B.25d.txt
   
   d)	–choice :
   		to run the model with glove file enter ‘g’ or to  run the model with tf idf enter t
   
   e)	–evaluation : enter ‘k’ for k fold cross validation or ‘a’ for accuracy 
   f)	–classifier : enter svc for support vector machines ,gbc for gradient boosting classifier, rfc for random forest 	    classifier