By Shashank Agarwal (agarwal.202@osu.edu) Anurag Kalra (kalra.25@osu.edu)

All code is in the code folder. Report is in report folder (pdf & docx formats are provided)

##Instructions

1. First we extracted all files from the sgm files and saved it in a csv called "data.csv" input= files/*.sgm files file=cleanXML.jar output=files/pre_processing.csv

2. We then used this to remove all the stop words and saved the file to "out_file.csv" input=data.csv file = read.py output=out_file.csv

3. Now we do stemming and create two files a) Frequency of each word across all documents : word_out_2.csv b) Number of documents where the word is present: word_out.csv input=out_file.csv file=read_py.py output=word_out.csv & word_out_2.csv

4. We then calculated the tf-idf of each word and saved it to the file 'tdidf.csv'

input= word_out.csv & word_out_2.csv file=tdidf.py output = tdidf.csv

5. We use only the words with tf-idf of greate than 0.01, which results in 2823 words input=tdidf.csv file=feature.py output=final_tdidf.csv

6. We then create the feature vector using the list of words as one axis and the document body as the other. Final results are stored in 'final_tdidf.csv' input = final_tdidf.csv, data.csv file=create_feature.py output=feature_matrix.pytext

Using the Make File:

To execute all steps just run "make All"

To execute a specific step from the above list follow these commands:

for step1 (create a csv file from sgm files) make step1

for step2 (remove stop words) make step2

for step3 (stemming and counting words) make step3a make step3b

for step4 (calculate tf-idf) make step4

for step5 (get top keywords with tf-idf > 0.1) make step5

for step6 (create feature vector) make step6

to clean all csv files use (please use this with caution as data generation takes a lot of time) make clean