Are you also a beginner to news API? And find it hard to understand the in-depth ideas of news intelligence? If you’re new here and looking forward to learning the basics of what news API is all about then you’ve come to the right place. I’m here to help you overcome some challenging terms and acronyms you might not have heard before.
To make it easy for you all, I’ve assembled 10 commonly used news API terms translated in simple language. Let’s get started with the technical terms you need to know before you move ahead to work on any news API.
1. Web Scraping
All that information you receive daily is a part of web scraping. With news intelligence terms, it is the process of extracting articles from news websites. Hence including data like title, body text, metadata, and other information. In-built bots are installed to measure greater efficiency and to avoid time consumption. To access real-time news data, news API extracts millions of news articles from across the globe with the support of web scraping.
2. Natural Language Processing
Have you thought about how the internet takes your command and shows you familiar results? Let’s get into the functioning behind it. Natural language processing works on the interactions between computers and human language. It also has the ability to imitate the understanding of human language. News APIs provide businesses with new opportunities through scraping news articles from the web and applying NLP to them.
3. Structured/Unstructured Data
By the name you all can assume that Structured data represents information in a systematic order and are placed in storage i.e relational database. Whereas Unstructured data is disorganized. News articles are written in ‘Human Language’ that is unstructured data. Businesses can gain insights much more efficiently and effectively if they start using structured data in their models.
4. Information Extraction/Parsing
Information extraction is scraping relevant information from both structured and unstructured data sources i.e extracting news articles from websites with the use of the NLP process. Parsing or syntactic analysis in NLP examines the article following the rules of grammar and transforms unstructured text into meaningful structured data format such as title, author, text, source.
5. Named Entity Recognition
Named Entity Recognition is a subfield of Information Extraction that tracks and extracts named entities from structured and unstructured data, like names of the people, organizations, places, products, etc. In this process, it detects the subject of an entity along with its surrounding text, and then the entity is easily identified to a knowledge base for illustration purposes.
Let’s take an example for your clear understanding. When you type ‘apple’ on the search box, you find a variety of search results appearing on your screen. It may be the company or the fruit. With possible filtration, it removes unwanted entities such as the fruit, not the company from your results. Its purpose is to detect the meaning of words in a context. Wikipedia as a third-party knowledge base can be used to provide cross-reference entities as a part of this process.
7. Event Detection
The name is kind of self-explanatory. Even so for those who have come across the term for the first time, event detection clusters articles covering the same events or topics in real-time. It also improves the efficiency and accuracy of breaking news and events detected.
8. Sentiment Analysis
Sentiment Analysis also known as opinion mining uses a combination of machine learning and the Natural Language Processing technique. It focuses on analyzing the emotional tone in a piece of text conveyed by the author. For a better understanding, let’s assume the author mentions a text that says, “I really like the new design of your website.” Here the statement sounds to be positive because of the acceptance of the subject. Moreover, businesses can gain value of their brands, products, or services with the support of sentiment analysis. Since this tool is also helping customers to express their emotions as feedback to the entity. Therefore it is becoming necessary to understand their sentiments using sentiment analysis. Let’s say when you automatically analyze 3000+ reviews of your company’s product. This could easily help you out in finding if your customers are happy with your pricing plans and customer service.
9. Time series analysis
A Time Series is an arrangement of data points mapped over a particular time. This allows users to easily understand the data and analyze time-stamped data in a meaningful format. For instance, it can be beneficial in measuring the variation of story volume about a specific entity that was positive or negative over time.
10. Trend Analysis
Trend Analysis is simply spotting the most frequently mentioned keywords or entities. There’s a choice to set limitations while using this analysis such as time. Therefore it will show you results as the most featured attributes.
I hope now you have a clear understanding of all the technical aspects that make a news API. The reason for making a separate blog on this was also necessary because we all at one point are trainees, and we do require basic fundamentals. I expect this information will be of some value to you and can help you in the long run while you create a news API for yourself or your business outlook.