python task1.py

Produces CSV with PID,PDFID,title,conf,folder,year,affil,authors,author_ids,keywords

Reads from: microsoft/Authors.txt, microsoft/PaperAuthorAffiliations.txt, microsoft/PaperKeywords.txt, microsoft/index.txt, microsoft/Papers.txt
Writes to: integrated.csv (stores the cleaned index.txt)

python keywords.py
python task2.py
	
Output:

	Collecting words from documents...
	Calculating support for each word...
	Finding Bigrams...
	Counting Bigrams...
	Finding Transactions...
	Preforming Apriori...
	Number of patterns: 34865
	
	Building non-single itemsets with FP-Growth...
	Number of patterns: 30902
	Done.

task2.py...
Reads from: microsoft/index.txt, text/*, microsoft/Papers.txt
Writes to: data/phrase2count.txt (each entity candidate & its count), data/paper2attributes.txt (contains paper id and entity names found in it), data/paper2attributes2label.txt (attributes, paper id, and name of conference)

python task3.py
(must run task 2 before task 3 in order to generate phrase2count.txt)

Reads from: microsoft/index.txt, text/*, data/phrase2count.txt
Writes to: data/entitytyping.txt (classification of each entity), 

python task4.py

Reads from: microsoft/Authors.txt, microsoft/PaperAuthorAffiliations.txt, 
Writes to: data/authorcollaborations.txt (Authors collaborations, support)

python task5.py

Reads from: microsoft/PaperKeywords.txt, microsoft/stopwords.txt, text/*, 
Writes to: data/our_keywords.txt (keywords we found using the ResponseBot functions), data/keyword_bigrams.txt (bigrams from our keywords), data/keyword_counts.txt (contains results of non-single itemsets), data/onetomany.txt (contains one-to-many association rules)

python task6.py
** task 2 must be run before this task in order to create necessary files **

Reads from: data/paper2attributes2label.txt, 
Writes to: data/bestattributes.txt (top entitites)

python task7.py

Reads from: text/*
Writes to:

python word_cloud_viz.py

Reads from: data/entitytyping.txt
Writes to: wordcloud.png