This article will talk about the importance of incorporating Text Analysis APIs with Natural Language Processing. But first, let’s start with the basics.
What is Natural Language Processing?
NLP is part of the Computational Science and Artificial Intelligence field. It focuses on the study of human language. Hence, this language provides computers with the capacity of reading and understanding our text and spoken words.
Therefore, Natural Language Processing gives users the ability to be helped by a computer. For example: when you type something and it autocompletes, or use speech recognition and the phone understands you completely. Yes, that’s all possible because of NLP.
What is an API?
An API is an Application Programming Interface that extracts and sends detailed information to softwares through reliable and immediate actions. That being said, we can start to understand why APIs and NLP come as a combo right? APIs use this language to help you complete easy and day-to-day tasks.
Consequently, I recommend you some of the best Text Analysis Apis with Natural Language Processing.
Klazify
Klazify is one of the most accurate online data collection solutions for email, URL, and domain. You can obtain any kind of details, sources, and statistics you may want to have. Such as logos, codes, locations; really any type of elements that may come to your mind right now.
Klazify has a Smart Categorization Technology. This API uses a machine learning (ML) engine to scan a website’s content and meta tags. It extracts text to classify the site and assigns up to three categories aided by natural language processing (NLP).
IBM Watson API
The IBM Watson Natural Language Classifier API allows you to interpret natural language using custom text classifiers. This API is one of the most popular on the market. You can use the IBM Watson API to extract insights from texts, understand the sentiment, add automation in workflows, and enhance search and discovery.
Lingua Kit
The API allows you to complete a wide range of NLP tasks, including language detection, named entity recognition, sentiment analysis, keyword extraction, multiword extraction, tokenization, syntactic analysis, and verb conjugation.
Now that you have the tools, all it takes is to try them and choose the one that fists the best for you.