Welcome to our Yelp Analysis project!
This is a non-commercial research project that aims to analyze and draw insights from the comprehensive dataset provided by Yelp, which you can access here:
https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset
or here:
You must download the Yelp data set. I downloaded it from the kaggle link above.
then you must specify the path to each file in the src/main/resources/application.yaml.
data:
businessfile: path/to/your/yelp_academic_dataset_business.json
checkinfile: path/to/your/yelp_academic_dataset_checkin.json
userfile: path/to/your/yelp_academic_dataset_user.json
reviewfile: path/to/your/yelp_academic_dataset_review.jsonYou must start up the services provided by docker-compose.yml.
docker-compose.yml file is provided.
Step 1: Make sure Docker Desktop is installed on your machine. If it's not, you can download it from the official Docker website.
Step 2: Open the directory where the docker-compose.yml file resides in your terminal (PowerShell or Command Prompt on Windows).
Step 3: Run the following command:
docker compose up -dThis command will tell Docker to download and provision all necessary resources using the docker-compose.yml file.
Now, start the Spring application using Maven
./mvnw spring-boot:runTo stop the application, press the following keys:
ctrl + c
Yelp has given 5 files where each line contains a single record in JSON format. Something that is missing is a published JSON schema that describes the definitions of what is allowed to appear in the properties of any given JSON. A schema like this will eliminate any ambiguity about the data. (i.e. we can know when any given data is invalid by definition or if our interpretation of the data is wrong)
For example, a single JSON record from yelp_academic_dataset_business.json appears to have
a attributes key in the property. The attributes property very clearly is of object type
(i.e. it can contain properties), but there is no documentation provided by Yelp that
enumerates the valid/expected properties and what they look like. It does make intuitive sense that
attributes is made to be an object, because it is very easy to include a brand-new attribute about
the business at any time.
The Yelp documentation for this data has the following:
// object, business attributes to values. note: some attribute values might be objects
"attributes": {
"RestaurantsTakeOut": true,
"BusinessParking": {
"garage": false,
"street": true,
"validated": false,
"lot": false,
"valet": false
},
},Therefore, attribute can contain any key name with it's value being
data type (e.g. string, number, another object...). Java/Kotlin, Spring, and Jackson (Spring's default JSON serialization/deserialization library)
are perfectly capable of handling this type of dynamic data. Take a look at the data type that was given
to attributes in the model Business.kt:
val attributes: Map<String, Any>?Business.kt represents the "blueprint" for how to construct and interact with a Business instance. All the Business JSONs will each be loaded into an object of this class and offer the same fields/capabilities. You can see that attributes is loaded into a map of key-value pairs. This supports a key name that could be anything and a value that could be any object.
The Open API Specification calls this key-value map a dictionary,
and it is capable of handling data with this behavior. There is an upcoming section giving the big picture of what
Open API Specification is. It would be nice if our API reported some of the "known" properties that can appear in
attributes.
This is the basis for the first data analysis: Enumerate Business Attributes
The current purpose of this code is to learn and practice coding. Especially with Java, Kotlin, and Spring. The following subsections will explain the technology being used in this project.
Spring AI makes it easy to enable applications with generative AI. It provides many things to accelerate integration many AI services into existing Spring applications.
Learn more about how this project uses Spring AI here:
Open API Specification (OAS) offers brilliant tools for rapid development of REST API servers. The key idea is you can describe the entire API from a single YAML file.
It is totally free! This repository demonstrates its use.
It is actually possible to use swagger.io to display the swagger docs for us, without needing to start the application server. The following link shows the Swagger UI docs for the API this server offers.
REST API for yelp-analysis Services
Feel free to contribute to this project by issuing a pull request. Any improvements or features you can offer to the project are welcome!
This project is open-source and available under the MIT License.
If you have any questions or would like more information about the project, please reach out!