* Charwad, Nayana Yashwant (ncharwad@umail.iu.edu)
* Chintala, Sri Laxmi (chintals@umail.iu.edu)
* Deshmukh, Renuka (renudesh@umail.iu.edu)
* Gaikwad, Aniket Sambhaji (anikgaik@umail.iu.edu)
* Indana, Venkata Prudhvi Raj (vindana@umail.iu.edu)

1. CreateTrainingAndTestCollections.java: 
	Imports the yelp data of business, review and tips into mongoDB and creates 60% training 
	collection 40% test collection. Each collection has business id, it's associated 
	categories, reviews and tips information.
			
2. GenerateIndex.java: 
	Creates training and test indexes using lucene. For each record in the training and test 
	collections in mongoDB, Fetches the business_id, categories, combines reviews and tips, 
	then adds it to the lucene document.

1. QueryOptimizer.java: 
	This class is used to iterate over every category (read from an input file), extract 
	tips and review information pertaining to a category from the train index, POS tag the 
	text and then extract the top query words for the category based on high TF*IDF score. 
	
	Important Functions :
	public void buildQueries(ArrayList<String> categories);
	public ArrayList<String> getNounsForCategory(String text);
	public String getConsolidatedReviewAndTipForCategory(TopDocs topdocs);

2. ScoreCalculator.java:
	This class takes all the nound words for a given category (after POS tagging), and 
	then coputes the TF*IDF score for each nouns and returns top n nouns to form a query 
	for the given category.
	
	Important Functions :
	public Double calculateScore(String nounword, ScoreDoc[] scoredocs);
	public ArrayList<String> getTopQueryWordsForCategory(ArrayList<String> nouns, TopDocs 
	topdocs, String category);
		

3. Tagger.java: 
	This class uses the stanford-postagger jar file to tag text and extract nouns based on 
	the english-left3words-distsim tagger model. 
	
	Important Functions :
	public ArrayList<String> tagText(String text);

1. CategoryPredictor.java: 
	This class read all the queries from a query file, run the queries on the test index 
	and predicts categories for business based, using different similarity algorithms.
	
	Important Functions :
	public void rank(ArrayList<String> queries, String algo);

2. CategoryBusinessMapping.java:
	This class is used to create the ground truth file of businesses and category mapping, 
	which is to be then used for final evaluation.  
	
	Important Functions :
	public void buildGroundTruthFile();

a. User-Business attribute model

	1.PreProcessing :

		Important Files :
		Execute 'scriptFileModifier.py'
		'FeatureGenerator.py'

		Important Functions :
		GenCat(dirPath)
		featureGen()
		featureSelect()
		applyPreProc()

	2.Classifiers :

		Important Files :
		Execute 'script_classify.py'
		'algorithms.py'

		Important Functions :
		runClassifier(fileName1,fileName2,1,flag)

b. Sentiment Analysis
	Class Name: ComputeUnigramFeatures
		This class is used to compute unigram features for sentiment analysis. Bag of words 
		assumption is used to compute frequencies of each unigram feature generated. Lucene 
		is used to remove stop words, tokenization and generation of unigram features from 
		review text.

	Class Name: ComputeBiGramFeatures
		This class is used to compute bigram features for sentiment analysis. Bag of words 
		assumption is used to compute frequencies of each bigram feature generated. Lucene 
		is used to remove stop words, tokenization and generation of bigram features from 
		review text.

	Class Name: FeatureCreation
		This class is used to generate frequency count for each feature generated through 
		feature generation program. This program computes frequencies for both bigrams as 
		well as unigrams. In multiple experiments, feature frequencies were also generated 
		for individual unigrams and bigrams.

	R File: SentimentAnalysis.R
		This file contains code to analyze train data class distribution, visualization of 
		word cloud for generated features as per ratings and run different regression 
		algorithms.

1. Task 2 Evaluation.py
	Similar python code is used to generate recall and precision there with different files.

2. Task 2 Evaluation.py
	This file contains code used to calculate different metrics used in Task 2

3. Other files used for generating graphs.