The Yelp dataset is open sourced, and it has enough information about each local business, reviews about those businesses, tips written by the user about a local business and photos uploaded by the user about the local business. This dataset can help us summarize useful information about most popular and unpopular restaurants classified by food genre and user reviews. Our goal is handle the following use-cases: Using user profile and his/her taste, we would recommend restaurants using his/her geospatial information in conjunction with collaborative filtering. We would perform sentimental analysis on the recommended restaurants reviews

1. YELP RECOMMENDATION SYSTEM AND SENTIMENT
ANALYSIS
DEVESH KANDPAL
SOHAM DASNEOGI

2. YELP DATASET
 The Yelp dataset is a subset of businesses, reviews and user data
 Each file consists of a single object type, one JSON-object-per-line
 Yelp aggregates review data from its users and ranks restaurants based on them

3. GOALS
 Using user profile, we would recommend restaurants using geospatial location in conjunction with
collaborative filtering
 Using the user reviews, we would performed sentimental analysis on the recommended restaurant
reviews.

4. SYSTEM ARCHITECTURE

5. TECHNOLOGY STACK
 Apache Spark (PySpark) for data cleaning and segregation
 Flask for buiding the web application
 MongoDB for user and restaurant details apart from storing the results of Option Mining and Sentiment
Analysis done using LDA
 Spark ML for ALS Matrix Factorization Model
 Genism for Topic Modeling on restaurant reviews and sentiment polarity
 Google Cloud Compute for hosting the data pipeline

6. RECOMMENDER SYSTEM
 The two most common types of recommender systems are Content-Based and Collaborative Filtering
 Collaborative Filtering produces recommendations based on the knowledge of users’ attitude to items,
that is it uses the wisdom of the crowd to recommend items
 Content based recommender system focus on the attributes of the items and give you
recommendations based on the similarity between them

7. COLLABORATIVE FILTERING
 Collaborative Filtering is most commonly used because it usually gives better results.
 The algorithm has the ability to do feature learning i.e it can start to learn for itself what features to use
for itself
 Collaborative Filtering is further divided into MEMORY-BASED CF and MODEL-BASED CF

8. MODEL BASED COLLABORATIVE FILTERING
 Model-Based CF is based on matrix factorization which is an unsupervised learning method for latent
variable decomposition and dimensionality reduction
 We are using ALTERNATING LEAST SQUARES (ALS) Matrix Factorization method
 ALS works by trying to find out the optimal representation of a user and a product matrix
 The genius part of ALS is that it alternates between finding the optimal values for the user matrix and the
product matrix

9. ALTERNATING LEAST SQUARES
 The ALS modelling is an unsupervised technique and takes into consideration latent factors.
 Our model is build on three features user business and reviews
 We construct a Matrix Factorization model using these three features
 Resulting model is capable of answering the following:
 1. Products for Users
 II. Users of a given Product
 We evaluate the model on basis of RMSE score

10. STEPS FOR ALS MODELLING
 For each state, we load the json file containing a combined view of user reviews and restaurants
 We select user_id, business_id, and rating and do a 80:20 random split on the data
 The training data is used to train the ALS model
 The tesing dats is used to compute the RMSE
 We get and RMSE score of 1.3
 The models are pickled and saved so that it can be consumed by the flask application.

11. SENTIMENT ANALYSIS
 Sentiment Analysis is opinion mining and a process to determining whether a piece of review is
POSITIVE NEGATIVE or NEUTRAL.
 It is deriving the opinion of the writer.
 We have used Latent Dirichlet Analysis (LDA) to mine the user reviews. The idea is to showcase the what
people in the consolidated are talking about so that it gives a high level view to the business users
without getting into the details of reading each review

12. STEPS FOR SENTIMENT ANALYSIS
 Combine all reviews of a single restaurant into a single document
 The entire document is tokenized and cleaned using stop words provided in the gensim library.
 We then used textblob on this cleaned text to determine sentiment polarity
 We then used gensim library for first creating a dictionary followed by corpus by using the cleaned text
 Once the corpus and dictionary are created, we used an unsupervised learning technique called LDA for topic
modelling
 We repeat this process for all the restaurants across all the states
 We then use LDA model to identify the top topics in the corpus and store the top 15 tokens in each topic in a
dictionary which is then dumped into mongo db along with the calculated polarity of the reviews and other
details

13. GOOGLE CLOUD PLATFORM

14. ROLE
 Devesh
 EDA on business.json
 Prepocessing by splitting and combining using spark job
 Model building - LDA Opinion Mining for each restaurant and dumping to MongoDB
 Getting data from mongodb against restaurants to view the results of opinion mining
 Cloud Deployment including setting up of Java, Scala, Spark, MongoDB and Flask Application
 Soham
 EDA on business.json, user.json, review.json
 Prepocessing data
 Model building – SVD and ALS model and pickling models
 Building UI components with dynamic page navigation
 Backend flask logic for functionalities