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
Web scraping is the process of extracting news articles from websites. It includes data like title, body text, metadata, and other information. It also helps news API by aggregating thousands of news articles from across the globe in real-time.
2. Natural Language Processing
Natural language processing works on the interactions between computers and human language. It also can imitate the understanding of human language. News APIs provide businesses with new opportunities through scraping news articles from the web along with applying NLP and machine learning techniques to them.
3. Structured/Unstructured Data
Structured data represents information in a systematic order and is placed in a 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 the scraping of relevant information from both structured and unstructured data sources. Parsing or syntactic analysis scans the news articles following the rules of grammar and transforms unstructured text into a 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. 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.
6. Disambiguation
Its purpose is to detect the meaning of words in a context. With possible filtration, it removes unwanted entities from your search results. Wikipedia as a third-party knowledge base can be used to provide cross-reference entities as a part of this process.
7. Event Detection
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 is also known as opinion mining focuses on analyzing the emotional tone in a piece of text conveyed by the author. Businesses can gain the 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 has become necessary to understand their belief using sentiment analysis.
9. Time series analysis
Time Series refers to the arrangement of data series points located over a certain time. This lets customers simply apprehend the data and analyze data according to date and time in a valuable presentation. 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.
Conclusion
Now when you know all the technical aspects that make a news API. It’s time to check into our Newsdata.io website for a free news API trial and understand the terms practically. I hope it can help you in the long run while you create a news API for yourself or your business outlook. The reason for making a separate blog on this was also necessary because we all at one point are trainees, and we do require fundamentals.
