1. SEARCH Final Project (ILS-Z534) Yelp Data Challenge
Under the Supervision of professor Xiaozhong Liu
Presented by,
Milind Gokhale
Namrata Jagasia
Deepak Bharanikana
Sameedha Bairagi
Siddharth Jayasankar

2. TASK – 1
Predicting Categories for each business using Information Retrieval Approach
Input : - Business ID
OutPut :- List of Categories for each Business ID

3. Dataset Division 1.6M reviews and 500K tips for 61K businesses
Test Set
Training Set
1.6M reviews and 500K tips for 61K businesses
Data divided into training set and test set.
66% Training Set: ~ 38K businesses
Used for category feature extraction.
33% Test Set: ~ 20K businesses
Used for prediction
Evaluation

4. Toolset POS Tagging Indexing + Search String Utilities Database
JSON Handling
Indexing + Search
String Utilities
POS Tagging
Database
Java (Eclipse)

5. Task 1 – ALGORITHM Start End Indexing [Business ID, Reviews, Tips]
Create Category Feature Map
Perform Business search on categories
Rank categories found
Evaluate precision and recall
Comparison with Ground Truth
End

6. Task 1 – Method Index Creation using Lucene
Category Feature Extraction from training set
Business ID
Category
Reviews and Tips Text
10001
Restaurant , Indian, Spicy
The chicken curry is great.
Loved the food. ……
10002
Restaurant, American, Donut
The donuts are delicious
The ambiance is good
Category
Search Query
Indian
Curry ,mutter, spicy…..
Italian
Pizza, Pasta, Alfredo…..
Features are words with highest TFIDF score among all the words in reviews and tips text for the category

7. Task 1 – Method Category Scores for Businesses Predicted Results 10001
Business ID
Result
10001
1 Indian
2 Restaurant – 0.678
3 Asian – 0.567 .
783 Mexican – 0.0
10002
Donut -0.67
Cheese – 0.56
Restaurant – 0.43
Bar – 0.0
Business ID
Predicted categories
10001
Indian, Restaurant, Asian, Authentic, traditional
10002
Donut , Cheese Restaurant , American , Icecream

8. Task 1 – Evaluation
Comparison of Ground Truth Value (provided by Yelp) with calculated predictions.

9. Task 1 – Evaluation

10. Task 1 – Evaluation

11. Predict MOST DISCUSSED Attributes
Task 2
Predict MOST DISCUSSED Attributes
In each city
Input : City Name
Output : List of Attributes That are most Talked about in the city

12. Task 2 - Algorithm Start End
Split the data into Test and Train and Index the reviews and Tips for each City separately
Using word net Create a Attribute Map for each Attribute with Attribute Name as key and search text (related words) as values
For the given input city , perform a search for each Attribute and retrieve scores and rank for each Attribute using BM25 ranking function. Perform this step on both test and train data
Assign top 10 ranked Attributes to each City for both test and train data
Calculate Precision and Recall for this model
Compare the test results with the train results.
End

13. Final Collection Used to Index
Task 2 - Method
Splitting and Indexing of data (City-wise)
Business File
Review File
Tip File
TRAIN INDEXES
TEST INDEXES
Reviews & Tips
Review 1
Review 2 .
Tip 1
Reviews & Tips
Review 101 Review 102 .
Tip 101
Tempe
Tempe
Reviews & Tips
Review 1
Review 2 .
Tip 1
Reviews & Tips
Review 101 Review 102 .
Tip 101
Pheonix
Pheonix
MongDB Collections
Reviews & Tips
Review 1
Review 2 .
Tip 1
Reviews & Tips
Review 101 Review 102 .
Tip 101
Las Vegas
Las Vegas
Final Collection Used to Index
{BusinessID : “1001” , City : “Las Vegas”, Rev&Tips :[“Rev1”,”Rev2”,…..,”Tip1”,”Tip2”,…]}

14. Task 2 - Method We used word net to create a Attribute map.
For the given city we ran a search for each Attribute on both test and train data and we retrieved the top 10 Attributes for both test and train data
Attribute Map
Good for Kids
Healthy, colorful, son, daughter,…….
Music
Jazz, Rock, Pop, melody…..
Liquor
Alcohol , sprits ,vodka, Rum….
Smoking
Cigar , Cigarette, lighter, …..
Attribute
Good for Kids
Music
Liquor
Smoking
WORD NET
Top 10 Attributes
Liquor
Good for Kids – 3.5
Music – 2.1
Smoking – 1.6
Results from Train Data
IR Model
Train Data
(60%)
Top 10 Attributes
Liquor
Music – 4.0
Good for Kids – 2.5
Smoking – 0.5
Results from Test Data
Test Data
(40%)

15. Task 2 - Evaluation
We compared the predicted results of test data with the predicted results of the train data (considered as ground truth)and calculated the precision and recall
Charlotte
Phoenix
Las Vegas

16. Challenges Task 1 Task 2 Data cleaning and pre-processing time.
Even after stop word removal, many unwanted features with high TFIDF scores.
Java heap space out of memory exception while feature extraction from categories.
Task 2
Data cleaning and pre-processing.
Manual removal of some features from WordNet for improving output.
Evaluation metric

17. Questions ?

18. Thank You…!!