Book Image

Angular Services

By : Sohail Salehi
Book Image

Angular Services

By: Sohail Salehi

Overview of this book

A primary concern with modern day applications is that they need to be dynamic, and for that, data access from the server side, data authentication, and security are very important. Angular leverages its services to create such state-of-the-art dynamic applications. This book will help you create and design customized services, integrate them into your applications, import third-party plugins, and make your apps perform better and faster. This book starts with a basic rundown on how you can create your own Angular development environment compatible with v2 and v4. You will then use Bootstrap and Angular UI components to create pages. You will also understand how to use controllers to collect data and populate them into NG UIs. Later, you will then create a rating service to evaluate entries and assign a score to them. Next, you will create "cron jobs" in NG. We will then create a crawler service to find all relevant resources regarding a selected headline and generate reports on it. Finally, you will create a service to manage accuracy and provide feedback about troubled areas in the app created. This book is up to date for the 2.4 release and is compatible with the 4.0 release as well, and it does not have any code based on the beta or release candidates.
Table of Contents (15 chapters)
Angular Services
About the Author
About the Reviewer
Customer Feedback

Clustering and similarity - retrieving documents of interest

The main questions are how do we measure similarity and how do we query over articles? If you think about it, we need to have some kind of scale (or a model if you like) to decide whether a specific document is close enough (similar) to our selected article.

Perhaps the simplest way to measure similarity between articles is count the similar words. We can simply create an object in which each word in the document will be the key, and the number of occurrences of that word in the document will be the value:

similarity-factor: { word1: 5 times, word2: 3 times,  ...} 

Then we can have an array of those objects for each document and use it as a factor for measuring similarity between two documents. For example, we take the following paragraph (from CNN news):


"Billy Bush ashamed of Donald Trump tape. Angry comments piled up on Billy Bush's Facebook page …"


Then we organize it into a data structure like the following figure: