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YelpWebScraping

Harvesting Yelp Data using API and Website - www.yelp.com

##Yelp Reference Links

Code Components:

  • config.py
  • getLocation.py
  • restaurants.py
  • scrapeDate.py
  • scrapeReviews.py

Initial Configuration

  1. Generate Yelp API key on https://www.yelp.com/developers/manage_api_keys
  2. Add the values of key generated to config.py
  3. Install required packages. Run pip install -r requirement.txt on terminal.
  4. After successful installation of above package, download the nltk corpus. Open terminal and type python and press Enter. Do following two steps. A new window will open. Select all identifier in window and click on download
> import nltk
> nltk.download()

Data Scraping for Popular Restarants

  1. Input File format: Sample input file can be found in input/SampleRestaurantInputFile

Minneapolis|Downtown Minneapolis|University St Paul|Cathedral Hill ```

  1. restaurant.py

This python program is a data scrapper that will fetch 40 records for each neighborhood in city mentioned in Input file. Here, we import getLocation to get longitude and latitude for each neighborhood through Google Geocoding API. The logic for getting location coordinates is written in getLocation.py. The output file generated by the program will be in output/SEARCHTERM.csv. With context of this project, the SEARCHTERM is restaurants. The file usage is as show below:

```      
    $ python restaurant.py [-h] -f FILENAME -s SEARCHTERM
      Scrape Data through Yelp API
      optional arguments:
      -h, --help            show this help message and exit
      -f FILENAME, --fileName FILENAME
                             File containing neighbourhoods and their respective
                             cities in pipe-delimited fashion
      -s SEARCHTERM, --searchTerm SEARCHTERM
                             Search Category for the data. eg. restaurants, bars, etc.
```
  1. scrapeDate.py

This python program will fetch first review date for the restaurants from Yelp website. The program takes input a file with restaurant IDs for which you need the first review date. If there are no reviews or reviews aren't in English language, the program would insert 99999 as dummy value. The output file generates would be output/firstReviewDate.csv The file usage is as shown below:

```
  $ python scrapeDate.py [-h] -f FILENAME

  optional arguments:
  -h, --help            show this help message and exit
  -f FILENAME, --fileName FILENAME
                        input file with restaurantIDs
```

Data Scraping for Reviews

  1. scrapeReviews.py

This python program will take location, category (eg. chinese, indian, mexican, etc.) and term (eg restaurants, bars, etc.) as input, and will fetch the user reviews which will suggest improvements for the restaurants. The filtered reviews will be output in reviewData file. The program usage is as shown below:

$ python scrapeReviews.py [-h] -l LOCATION -c CATEGORY -t TERM

  Scrape Reviews through Yelp API and Web
  optional arguments:
	-h, --help            show this help message and exit
	-l LOCATION, --location LOCATION
      	                Location where you want to search reviews
	-c CATEGORY, --category CATEGORY
                      	Category for the reviews. eg. chinese, indian, etc.
	-t TERM, --term TERM  Search by restaurant, bar, etc

Datasets Created:

  1. RestaurantData.xlsx - The raw dataset generated after running restaurant.py
  2. filteredRestaurantData1.xlsx - filtered dataset after removing duplicates and noise data (no location, zipcode,etc)
  3. filteredRestaurantData2.xlsx - filtered dataset after removing cities restaurants with restaurants less than 100
  4. filteredRestaurantData3.xlsx - final dataset for restaurants with review_count > 100 and rating > 4.5
  5. reviewData.xlsx - filtered reviews giving improvements for restaurants. This dataset is generated after text mining code scrapeReviews.py

Visualization Files and Codes:

  1. plotting distribution of rating and review count.R
  2. Tableau_Worbook.rar
  3. Yelp.R - impute missing vales using mice package
  4. Additionally each workbook in dataset above has a sheet called pivots which has analysis along with figures for that workbook.

Sample Files:

These files can be found in input folder. Place the files in same location as corresponding scripts.

  1. SampleRestaurantInputFile - used with restaurant.py
  2. sampleScrapeDateInputFile - used with scrapeDate.py